diff --git a/README.md b/README.md
index 7414280a..66119d84 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,6 @@
 </p>
 
 
-
 ## 📜 Description
 
 **Virny** is a Python library for auditing model stability and fairness. The Virny library was
diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb
new file mode 100644
index 00000000..14fb79b7
--- /dev/null
+++ b/docs/examples/Multiple_Models_Interface_Vis.ipynb
@@ -0,0 +1,324 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "248cbed8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-29T20:56:16.932083Z",
+     "start_time": "2023-09-29T20:56:16.278169Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7ec6cd08",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-29T20:56:16.940086Z",
+     "start_time": "2023-09-29T20:56:16.931485Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b8cb69f2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-29T20:56:16.951831Z",
+     "start_time": "2023-09-29T20:56:16.940588Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current location:  /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n"
+     ]
+    }
+   ],
+   "source": [
+    "cur_folder_name = os.getcwd().split('/')[-1]\n",
+    "if cur_folder_name != \"Virny\":\n",
+    "    os.chdir(\"../..\")\n",
+    "\n",
+    "print('Current location: ', os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a578f2ab",
+   "metadata": {},
+   "source": [
+    "# Multiple Models Interface Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a9241de",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-29T20:56:30.072450Z",
+     "start_time": "2023-09-29T20:56:22.772584Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from virny.utils.custom_initializers import read_model_metric_dfs, create_config_obj\n",
+    "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer\n",
+    "from virny.custom_classes.metrics_composer import MetricsComposer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "ROOT_DIR = os.path.join('docs', 'examples')\n",
+    "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n",
+    "config_yaml_content = \"\"\"\n",
+    "dataset_name: COMPAS_Without_Sensitive_Attributes\n",
+    "bootstrap_fraction: 0.8\n",
+    "n_estimators: 50  # Better to input the higher number of estimators than 100; this is only for this use case example\n",
+    "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n",
+    "\"\"\"\n",
+    "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n",
+    "    f.write(config_yaml_content)\n",
+    "\n",
+    "config = create_config_obj(config_yaml_path=config_yaml_path)\n",
+    "model_names = ['DecisionTreeClassifier', 'LogisticRegression', 'RandomForestClassifier', 'XGBClassifier']\n",
+    "SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', 'COMPAS_Without_Sensitive_Attributes_Metrics_20230812__224136')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-09-29T20:56:30.095448Z",
+     "start_time": "2023-09-29T20:56:30.073873Z"
+    }
+   },
+   "id": "d777610462304f63"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f94a20dc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-29T20:56:30.121865Z",
+     "start_time": "2023-09-29T20:56:30.094816Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=model_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b04d06cf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-29T20:56:30.139696Z",
+     "start_time": "2023-09-29T20:56:30.121071Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "be6ace22",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-29T20:56:30.169575Z",
+     "start_time": "2023-09-29T20:56:30.138633Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Compute composed metrics\n",
+    "models_composed_metrics_df = metrics_composer.compose_metrics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                   Metric      overall    sex_priv  sex_priv_correct  \\\n0                    Mean     0.524270    0.578645          0.600790   \n1                     Std     0.067963    0.073618          0.072201   \n2                     IQR     0.090596    0.099782          0.098402   \n3   Aleatoric_Uncertainty     0.834874    0.846689          0.826891   \n4     Overall_Uncertainty     0.859083    0.876581          0.856843   \n5        Statistical_Bias     0.405041    0.395811          0.314809   \n6                  Jitter     0.106917    0.132090          0.112864   \n7     Per_Sample_Accuracy     0.691061    0.711090          0.918452   \n8         Label_Stability     0.851667    0.807393          0.836903   \n9                     TPR     0.679406    0.613333          1.000000   \n10                    TNR     0.738462    0.801471          1.000000   \n11                    PPV     0.676533    0.630137          1.000000   \n12                    FNR     0.320594    0.386667          0.000000   \n13                    FPR     0.261538    0.198529          0.000000   \n14               Accuracy     0.712121    0.734597          1.000000   \n15                     F1     0.677966    0.621622          1.000000   \n16         Selection-Rate     0.447917    0.345972          0.296774   \n17          Positive-Rate     1.004246    0.973333          1.000000   \n18            Sample_Size  1056.000000  211.000000        155.000000   \n\n    sex_priv_incorrect     sex_dis  sex_dis_correct  sex_dis_incorrect  \\\n0             0.517352    0.510692         0.514399           0.501767   \n1             0.077539    0.066551         0.064791           0.070788   \n2             0.103600    0.088303         0.085977           0.093900   \n3             0.901488    0.831924         0.817170           0.867440   \n4             0.931213    0.854713         0.839203           0.892051   \n5             0.620012    0.407346         0.301656           0.661771   \n6             0.185306    0.100631         0.091351           0.122972   \n7             0.137143    0.686059         0.936918           0.082177   \n8             0.725714    0.862722         0.873970           0.835645   \n9             0.000000    0.691919         1.000000           0.000000   \n10            0.000000    0.719376         1.000000           0.000000   \n11            0.000000    0.685000         1.000000           0.000000   \n12            1.000000    0.308081         0.000000           1.000000   \n13            1.000000    0.280624         0.000000           1.000000   \n14            0.000000    0.706509         1.000000           0.000000   \n15            0.000000    0.688442         1.000000           0.000000   \n16            0.482143    0.473373         0.458961           0.508065   \n17            0.931034    1.010101         1.000000           1.032787   \n18           56.000000  845.000000       597.000000         248.000000   \n\n     race_priv  race_priv_correct  ...  race_dis_correct  race_dis_incorrect  \\\n0     0.597526           0.618185  ...          0.473863            0.484344   \n1     0.069162           0.066865  ...          0.065947            0.070060   \n2     0.093184           0.089451  ...          0.087919            0.091258   \n3     0.821672           0.807043  ...          0.827404            0.880296   \n4     0.847778           0.832001  ...          0.850193            0.903737   \n5     0.393484           0.296788  ...          0.309510            0.650314   \n6     0.107225           0.097218  ...          0.094812            0.134214   \n7     0.708261           0.930526  ...          0.934866            0.091340   \n8     0.848213           0.861316  ...          0.869732            0.817320   \n9     0.585034           1.000000  ...          1.000000            0.000000   \n10    0.816479           1.000000  ...          1.000000            0.000000   \n11    0.637037           1.000000  ...          1.000000            0.000000   \n12    0.414966           0.000000  ...          0.000000            1.000000   \n13    0.183521           0.000000  ...          0.000000            1.000000   \n14    0.734300           1.000000  ...          1.000000            0.000000   \n15    0.609929           1.000000  ...          1.000000            0.000000   \n16    0.326087           0.282895  ...          0.522321            0.536082   \n17    0.918367           1.000000  ...          1.000000            1.155556   \n18  414.000000         304.000000  ...        448.000000          194.000000   \n\n    sex&race_priv  sex&race_priv_correct  sex&race_priv_incorrect  \\\n0        0.586391               0.607290                 0.529874   \n1        0.068718               0.066018                 0.076019   \n2        0.092020               0.088338                 0.101975   \n3        0.832383               0.817398                 0.872906   \n4        0.857995               0.841790                 0.901818   \n5        0.396398               0.302520                 0.650263   \n6        0.108871               0.095304                 0.145559   \n7        0.708783               0.933073                 0.102254   \n8        0.847224               0.866354                 0.795493   \n9        0.595745               1.000000                 0.000000   \n10       0.804734               1.000000                 0.000000   \n11       0.629213               1.000000                 0.000000   \n12       0.404255               0.000000                 1.000000   \n13       0.195266               0.000000                 1.000000   \n14       0.730038               1.000000                 0.000000   \n15       0.612022               1.000000                 0.000000   \n16       0.338403               0.291667                 0.464789   \n17       0.946809               1.000000                 0.868421   \n18     526.000000             384.000000               142.000000   \n\n    sex&race_dis  sex&race_dis_correct  sex&race_dis_incorrect  \\\n0       0.462617              0.453857                0.482517   \n1       0.067213              0.066631                0.068536   \n2       0.089184              0.088747                0.090175   \n3       0.837346              0.821026                0.874418   \n4       0.860162              0.843933                0.897027   \n5       0.413620              0.306294                0.657422   \n6       0.104978              0.096287                0.124722   \n7       0.673472              0.933152                0.083580   \n8       0.856075              0.866304                0.832840   \n9       0.734982              1.000000                0.000000   \n10      0.647773              1.000000                0.000000   \n11      0.705085              1.000000                0.000000   \n12      0.265018              0.000000                1.000000   \n13      0.352227              0.000000                1.000000   \n14      0.694340              1.000000                0.000000   \n15      0.719723              1.000000                0.000000   \n16      0.556604              0.565217                0.537037   \n17      1.042403              1.000000                1.160000   \n18    530.000000            368.000000              162.000000   \n\n                Model_Name                                       Model_Params  \n0   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n1   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n2   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n3   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n4   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n5   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n6   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n7   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n8   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n9   RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n10  RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n11  RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n12  RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n13  RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n14  RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n15  RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n16  RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n17  RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n18  RandomForestClassifier  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  \n\n[19 rows x 22 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metric</th>\n      <th>overall</th>\n      <th>sex_priv</th>\n      <th>sex_priv_correct</th>\n      <th>sex_priv_incorrect</th>\n      <th>sex_dis</th>\n      <th>sex_dis_correct</th>\n      <th>sex_dis_incorrect</th>\n      <th>race_priv</th>\n      <th>race_priv_correct</th>\n      <th>...</th>\n      <th>race_dis_correct</th>\n      <th>race_dis_incorrect</th>\n      <th>sex&amp;race_priv</th>\n      <th>sex&amp;race_priv_correct</th>\n      <th>sex&amp;race_priv_incorrect</th>\n      <th>sex&amp;race_dis</th>\n      <th>sex&amp;race_dis_correct</th>\n      <th>sex&amp;race_dis_incorrect</th>\n      <th>Model_Name</th>\n      <th>Model_Params</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Mean</td>\n      <td>0.524270</td>\n      <td>0.578645</td>\n      <td>0.600790</td>\n      <td>0.517352</td>\n      <td>0.510692</td>\n      <td>0.514399</td>\n      <td>0.501767</td>\n      <td>0.597526</td>\n      <td>0.618185</td>\n      <td>...</td>\n      <td>0.473863</td>\n      <td>0.484344</td>\n      <td>0.586391</td>\n      <td>0.607290</td>\n      <td>0.529874</td>\n      <td>0.462617</td>\n      <td>0.453857</td>\n      <td>0.482517</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Std</td>\n      <td>0.067963</td>\n      <td>0.073618</td>\n      <td>0.072201</td>\n      <td>0.077539</td>\n      <td>0.066551</td>\n      <td>0.064791</td>\n      <td>0.070788</td>\n      <td>0.069162</td>\n      <td>0.066865</td>\n      <td>...</td>\n      <td>0.065947</td>\n      <td>0.070060</td>\n      <td>0.068718</td>\n      <td>0.066018</td>\n      <td>0.076019</td>\n      <td>0.067213</td>\n      <td>0.066631</td>\n      <td>0.068536</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>IQR</td>\n      <td>0.090596</td>\n      <td>0.099782</td>\n      <td>0.098402</td>\n      <td>0.103600</td>\n      <td>0.088303</td>\n      <td>0.085977</td>\n      <td>0.093900</td>\n      <td>0.093184</td>\n      <td>0.089451</td>\n      <td>...</td>\n      <td>0.087919</td>\n      <td>0.091258</td>\n      <td>0.092020</td>\n      <td>0.088338</td>\n      <td>0.101975</td>\n      <td>0.089184</td>\n      <td>0.088747</td>\n      <td>0.090175</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Aleatoric_Uncertainty</td>\n      <td>0.834874</td>\n      <td>0.846689</td>\n      <td>0.826891</td>\n      <td>0.901488</td>\n      <td>0.831924</td>\n      <td>0.817170</td>\n      <td>0.867440</td>\n      <td>0.821672</td>\n      <td>0.807043</td>\n      <td>...</td>\n      <td>0.827404</td>\n      <td>0.880296</td>\n      <td>0.832383</td>\n      <td>0.817398</td>\n      <td>0.872906</td>\n      <td>0.837346</td>\n      <td>0.821026</td>\n      <td>0.874418</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Overall_Uncertainty</td>\n      <td>0.859083</td>\n      <td>0.876581</td>\n      <td>0.856843</td>\n      <td>0.931213</td>\n      <td>0.854713</td>\n      <td>0.839203</td>\n      <td>0.892051</td>\n      <td>0.847778</td>\n      <td>0.832001</td>\n      <td>...</td>\n      <td>0.850193</td>\n      <td>0.903737</td>\n      <td>0.857995</td>\n      <td>0.841790</td>\n      <td>0.901818</td>\n      <td>0.860162</td>\n      <td>0.843933</td>\n      <td>0.897027</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Statistical_Bias</td>\n      <td>0.405041</td>\n      <td>0.395811</td>\n      <td>0.314809</td>\n      <td>0.620012</td>\n      <td>0.407346</td>\n      <td>0.301656</td>\n      <td>0.661771</td>\n      <td>0.393484</td>\n      <td>0.296788</td>\n      <td>...</td>\n      <td>0.309510</td>\n      <td>0.650314</td>\n      <td>0.396398</td>\n      <td>0.302520</td>\n      <td>0.650263</td>\n      <td>0.413620</td>\n      <td>0.306294</td>\n      <td>0.657422</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Jitter</td>\n      <td>0.106917</td>\n      <td>0.132090</td>\n      <td>0.112864</td>\n      <td>0.185306</td>\n      <td>0.100631</td>\n      <td>0.091351</td>\n      <td>0.122972</td>\n      <td>0.107225</td>\n      <td>0.097218</td>\n      <td>...</td>\n      <td>0.094812</td>\n      <td>0.134214</td>\n      <td>0.108871</td>\n      <td>0.095304</td>\n      <td>0.145559</td>\n      <td>0.104978</td>\n      <td>0.096287</td>\n      <td>0.124722</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Per_Sample_Accuracy</td>\n      <td>0.691061</td>\n      <td>0.711090</td>\n      <td>0.918452</td>\n      <td>0.137143</td>\n      <td>0.686059</td>\n      <td>0.936918</td>\n      <td>0.082177</td>\n      <td>0.708261</td>\n      <td>0.930526</td>\n      <td>...</td>\n      <td>0.934866</td>\n      <td>0.091340</td>\n      <td>0.708783</td>\n      <td>0.933073</td>\n      <td>0.102254</td>\n      <td>0.673472</td>\n      <td>0.933152</td>\n      <td>0.083580</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Label_Stability</td>\n      <td>0.851667</td>\n      <td>0.807393</td>\n      <td>0.836903</td>\n      <td>0.725714</td>\n      <td>0.862722</td>\n      <td>0.873970</td>\n      <td>0.835645</td>\n      <td>0.848213</td>\n      <td>0.861316</td>\n      <td>...</td>\n      <td>0.869732</td>\n      <td>0.817320</td>\n      <td>0.847224</td>\n      <td>0.866354</td>\n      <td>0.795493</td>\n      <td>0.856075</td>\n      <td>0.866304</td>\n      <td>0.832840</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>TPR</td>\n      <td>0.679406</td>\n      <td>0.613333</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.691919</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.585034</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.595745</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.734982</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>TNR</td>\n      <td>0.738462</td>\n      <td>0.801471</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.719376</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.816479</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.804734</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.647773</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>PPV</td>\n      <td>0.676533</td>\n      <td>0.630137</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.685000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.637037</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.629213</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.705085</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>FNR</td>\n      <td>0.320594</td>\n      <td>0.386667</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.308081</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.414966</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.404255</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.265018</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>FPR</td>\n      <td>0.261538</td>\n      <td>0.198529</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.280624</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.183521</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.195266</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.352227</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Accuracy</td>\n      <td>0.712121</td>\n      <td>0.734597</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.706509</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.734300</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.730038</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.694340</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>F1</td>\n      <td>0.677966</td>\n      <td>0.621622</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.688442</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.609929</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.612022</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.719723</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Selection-Rate</td>\n      <td>0.447917</td>\n      <td>0.345972</td>\n      <td>0.296774</td>\n      <td>0.482143</td>\n      <td>0.473373</td>\n      <td>0.458961</td>\n      <td>0.508065</td>\n      <td>0.326087</td>\n      <td>0.282895</td>\n      <td>...</td>\n      <td>0.522321</td>\n      <td>0.536082</td>\n      <td>0.338403</td>\n      <td>0.291667</td>\n      <td>0.464789</td>\n      <td>0.556604</td>\n      <td>0.565217</td>\n      <td>0.537037</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Positive-Rate</td>\n      <td>1.004246</td>\n      <td>0.973333</td>\n      <td>1.000000</td>\n      <td>0.931034</td>\n      <td>1.010101</td>\n      <td>1.000000</td>\n      <td>1.032787</td>\n      <td>0.918367</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.155556</td>\n      <td>0.946809</td>\n      <td>1.000000</td>\n      <td>0.868421</td>\n      <td>1.042403</td>\n      <td>1.000000</td>\n      <td>1.160000</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Sample_Size</td>\n      <td>1056.000000</td>\n      <td>211.000000</td>\n      <td>155.000000</td>\n      <td>56.000000</td>\n      <td>845.000000</td>\n      <td>597.000000</td>\n      <td>248.000000</td>\n      <td>414.000000</td>\n      <td>304.000000</td>\n      <td>...</td>\n      <td>448.000000</td>\n      <td>194.000000</td>\n      <td>526.000000</td>\n      <td>384.000000</td>\n      <td>142.000000</td>\n      <td>530.000000</td>\n      <td>368.000000</td>\n      <td>162.000000</td>\n      <td>RandomForestClassifier</td>\n      <td>{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...</td>\n    </tr>\n  </tbody>\n</table>\n<p>19 rows × 22 columns</p>\n</div>"
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models_metrics_dct['RandomForestClassifier'].head(100)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-01T20:57:20.233976Z",
+     "start_time": "2023-10-01T20:57:20.133369Z"
+    }
+   },
+   "id": "54a73b4d053334b4"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                           Metric       sex      race  sex&race  \\\n0              Equalized_Odds_TPR  0.211919  0.195326  0.183576   \n1              Equalized_Odds_FPR  0.098356  0.104728  0.141078   \n2              Equalized_Odds_FNR -0.211919 -0.195326 -0.183576   \n3                Disparate_Impact  1.234115  1.135965  1.125105   \n4   Statistical_Parity_Difference  0.193535  0.123016  0.115123   \n5                 Accuracy_Parity  0.009832  0.006840 -0.010984   \n6           Label_Stability_Ratio  1.024740  0.997454  0.995869   \n7                      IQR_Parity  0.000768 -0.004804 -0.003282   \n8                      Std_Parity -0.005106 -0.000927 -0.001976   \n9                       Std_Ratio  0.931699  0.986984  0.972422   \n10                  Jitter_Parity -0.013818  0.007192  0.005364   \n11             Equalized_Odds_TPR  0.166465  0.258440  0.226205   \n12             Equalized_Odds_FPR  0.096129  0.156703  0.186079   \n13             Equalized_Odds_FNR -0.166465 -0.258440 -0.226205   \n14               Disparate_Impact  1.176075  1.341036  1.263916   \n15  Statistical_Parity_Difference  0.145556  0.262157  0.216187   \n16                Accuracy_Parity -0.010286 -0.003747 -0.024119   \n17          Label_Stability_Ratio  1.021988  0.988991  1.003152   \n18                     IQR_Parity  0.001712  0.001225  0.001058   \n19                     Std_Parity  0.000822  0.000278  0.000170   \n\n                Model_Name  \n0   DecisionTreeClassifier  \n1   DecisionTreeClassifier  \n2   DecisionTreeClassifier  \n3   DecisionTreeClassifier  \n4   DecisionTreeClassifier  \n5   DecisionTreeClassifier  \n6   DecisionTreeClassifier  \n7   DecisionTreeClassifier  \n8   DecisionTreeClassifier  \n9   DecisionTreeClassifier  \n10  DecisionTreeClassifier  \n11      LogisticRegression  \n12      LogisticRegression  \n13      LogisticRegression  \n14      LogisticRegression  \n15      LogisticRegression  \n16      LogisticRegression  \n17      LogisticRegression  \n18      LogisticRegression  \n19      LogisticRegression  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metric</th>\n      <th>sex</th>\n      <th>race</th>\n      <th>sex&amp;race</th>\n      <th>Model_Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Equalized_Odds_TPR</td>\n      <td>0.211919</td>\n      <td>0.195326</td>\n      <td>0.183576</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Equalized_Odds_FPR</td>\n      <td>0.098356</td>\n      <td>0.104728</td>\n      <td>0.141078</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Equalized_Odds_FNR</td>\n      <td>-0.211919</td>\n      <td>-0.195326</td>\n      <td>-0.183576</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Disparate_Impact</td>\n      <td>1.234115</td>\n      <td>1.135965</td>\n      <td>1.125105</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Statistical_Parity_Difference</td>\n      <td>0.193535</td>\n      <td>0.123016</td>\n      <td>0.115123</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Accuracy_Parity</td>\n      <td>0.009832</td>\n      <td>0.006840</td>\n      <td>-0.010984</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Label_Stability_Ratio</td>\n      <td>1.024740</td>\n      <td>0.997454</td>\n      <td>0.995869</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>IQR_Parity</td>\n      <td>0.000768</td>\n      <td>-0.004804</td>\n      <td>-0.003282</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Std_Parity</td>\n      <td>-0.005106</td>\n      <td>-0.000927</td>\n      <td>-0.001976</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Std_Ratio</td>\n      <td>0.931699</td>\n      <td>0.986984</td>\n      <td>0.972422</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Jitter_Parity</td>\n      <td>-0.013818</td>\n      <td>0.007192</td>\n      <td>0.005364</td>\n      <td>DecisionTreeClassifier</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Equalized_Odds_TPR</td>\n      <td>0.166465</td>\n      <td>0.258440</td>\n      <td>0.226205</td>\n      <td>LogisticRegression</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Equalized_Odds_FPR</td>\n      <td>0.096129</td>\n      <td>0.156703</td>\n      <td>0.186079</td>\n      <td>LogisticRegression</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Equalized_Odds_FNR</td>\n      <td>-0.166465</td>\n      <td>-0.258440</td>\n      <td>-0.226205</td>\n      <td>LogisticRegression</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Disparate_Impact</td>\n      <td>1.176075</td>\n      <td>1.341036</td>\n      <td>1.263916</td>\n      <td>LogisticRegression</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Statistical_Parity_Difference</td>\n      <td>0.145556</td>\n      <td>0.262157</td>\n      <td>0.216187</td>\n      <td>LogisticRegression</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Accuracy_Parity</td>\n      <td>-0.010286</td>\n      <td>-0.003747</td>\n      <td>-0.024119</td>\n      <td>LogisticRegression</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Label_Stability_Ratio</td>\n      <td>1.021988</td>\n      <td>0.988991</td>\n      <td>1.003152</td>\n      <td>LogisticRegression</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>IQR_Parity</td>\n      <td>0.001712</td>\n      <td>0.001225</td>\n      <td>0.001058</td>\n      <td>LogisticRegression</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Std_Parity</td>\n      <td>0.000822</td>\n      <td>0.000278</td>\n      <td>0.000170</td>\n      <td>LogisticRegression</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models_composed_metrics_df.head(20)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-01T11:29:37.410638Z",
+     "start_time": "2023-10-01T11:29:37.382980Z"
+    }
+   },
+   "id": "5798eb95fbeaea54"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deb45226",
+   "metadata": {},
+   "source": [
+    "## Metrics Visualization and Reporting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "id": "435b9d98",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-02T17:55:10.703782Z",
+     "start_time": "2023-10-02T17:55:06.041613Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n",
+    "                                          sensitive_attributes_dct=config.sensitive_attributes_dct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n",
+      "Keyboard interruption in main thread... closing server.\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.start_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-02T17:55:47.535767Z",
+     "start_time": "2023-10-02T17:55:10.703964Z"
+    }
+   },
+   "id": "678a9dc8d51243f4"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7860\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.stop_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-09-29T21:41:49.927075Z",
+     "start_time": "2023-09-29T21:41:49.639933Z"
+    }
+   },
+   "id": "277b6d1de837dab7"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "2326c129",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb
new file mode 100644
index 00000000..ecd29b0e
--- /dev/null
+++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb
@@ -0,0 +1,299 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "248cbed8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T22:37:44.370856Z",
+     "start_time": "2023-12-10T22:37:43.972175Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7ec6cd08",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T22:37:44.380242Z",
+     "start_time": "2023-12-10T22:37:44.371542Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b8cb69f2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T22:37:44.391659Z",
+     "start_time": "2023-12-10T22:37:44.380644Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current location:  /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n"
+     ]
+    }
+   ],
+   "source": [
+    "cur_folder_name = os.getcwd().split('/')[-1]\n",
+    "if cur_folder_name != \"Virny\":\n",
+    "    os.chdir(\"../..\")\n",
+    "\n",
+    "print('Current location: ', os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a578f2ab",
+   "metadata": {},
+   "source": [
+    "# Multiple Models Interface Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a9241de",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T22:37:45.918385Z",
+     "start_time": "2023-12-10T22:37:44.390547Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "from virny.datasets import ACSIncomeDataset\n",
+    "from virny.custom_classes.metrics_composer import MetricsComposer\n",
+    "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "data_loader = ACSIncomeDataset(state=['GA'], year=2018, with_nulls=False, subsample_size=15_000, subsample_seed=42)\n",
+    "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T22:37:47.214487Z",
+     "start_time": "2023-12-10T22:37:45.921391Z"
+    }
+   },
+   "id": "d3c53c7b72ecbcd0"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
+   "source": [
+    "ROOT_DIR = os.path.join('docs', 'examples')\n",
+    "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'income_subgroup_metrics.csv'), header=0)\n",
+    "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n",
+    "                                     subgroup_metrics_df['Intervention_Param'].astype(str))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T22:37:47.242581Z",
+     "start_time": "2023-12-10T22:37:47.214727Z"
+    }
+   },
+   "id": "2aab7c79ecdee914"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                         Metric       SEX     RAC1P  SEX&RAC1P  \\\n0               Accuracy_Parity  0.047756  0.074977   0.065217   \n1  Aleatoric_Uncertainty_Parity -0.039005 -0.011947  -0.009222   \n2   Aleatoric_Uncertainty_Ratio  0.935159  0.979638   0.984220   \n3            Equalized_Odds_FNR  0.030793 -0.110745  -0.052498   \n4            Equalized_Odds_FPR -0.021317  0.000952  -0.007008   \n\n                  Model_Name  \n0  LGBMClassifier__alpha=0.7  \n1  LGBMClassifier__alpha=0.7  \n2  LGBMClassifier__alpha=0.7  \n3  LGBMClassifier__alpha=0.7  \n4  LGBMClassifier__alpha=0.7  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metric</th>\n      <th>SEX</th>\n      <th>RAC1P</th>\n      <th>SEX&amp;RAC1P</th>\n      <th>Model_Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Accuracy_Parity</td>\n      <td>0.047756</td>\n      <td>0.074977</td>\n      <td>0.065217</td>\n      <td>LGBMClassifier__alpha=0.7</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Aleatoric_Uncertainty_Parity</td>\n      <td>-0.039005</td>\n      <td>-0.011947</td>\n      <td>-0.009222</td>\n      <td>LGBMClassifier__alpha=0.7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Aleatoric_Uncertainty_Ratio</td>\n      <td>0.935159</td>\n      <td>0.979638</td>\n      <td>0.984220</td>\n      <td>LGBMClassifier__alpha=0.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Equalized_Odds_FNR</td>\n      <td>0.030793</td>\n      <td>-0.110745</td>\n      <td>-0.052498</td>\n      <td>LGBMClassifier__alpha=0.7</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Equalized_Odds_FPR</td>\n      <td>-0.021317</td>\n      <td>0.000952</td>\n      <td>-0.007008</td>\n      <td>LGBMClassifier__alpha=0.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_names = subgroup_metrics_df['Model_Name'].unique()\n",
+    "models_metrics_dct = dict()\n",
+    "for model_name in model_names:\n",
+    "    models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n",
+    "\n",
+    "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n",
+    "models_composed_metrics_df = metrics_composer.compose_metrics()\n",
+    "models_composed_metrics_df.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T22:37:47.297089Z",
+     "start_time": "2023-12-10T22:37:47.240439Z"
+    }
+   },
+   "id": "44ee5eff6054ce04"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models_metrics_dct.keys()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T22:37:47.328697Z",
+     "start_time": "2023-12-10T22:37:47.295950Z"
+    }
+   },
+   "id": "15ed7d1ba1f22317"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deb45226",
+   "metadata": {},
+   "source": [
+    "## Metrics Visualization and Reporting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "435b9d98",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T22:37:47.374721Z",
+     "start_time": "2023-12-10T22:37:47.317882Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n",
+    "                                          models_metrics_dct, models_composed_metrics_df,\n",
+    "                                          sensitive_attributes_dct=sensitive_attributes_dct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n",
+      "Keyboard interruption in main thread... closing server.\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.start_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-11T00:26:17.429094Z",
+     "start_time": "2023-12-10T22:37:47.343749Z"
+    }
+   },
+   "id": "678a9dc8d51243f4"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7860\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.stop_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-11T00:26:17.482944Z",
+     "start_time": "2023-12-11T00:26:17.438287Z"
+    }
+   },
+   "id": "277b6d1de837dab7"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-11T00:26:17.483195Z",
+     "start_time": "2023-12-11T00:26:17.479725Z"
+    }
+   },
+   "id": "21c0ad91536f0af5"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb
new file mode 100644
index 00000000..3b630e94
--- /dev/null
+++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb
@@ -0,0 +1,298 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "248cbed8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:11:51.087426Z",
+     "start_time": "2023-12-18T17:11:50.720930Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7ec6cd08",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:11:51.096433Z",
+     "start_time": "2023-12-18T17:11:51.087934Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b8cb69f2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:11:51.105608Z",
+     "start_time": "2023-12-18T17:11:51.096820Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current location:  /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n"
+     ]
+    }
+   ],
+   "source": [
+    "cur_folder_name = os.getcwd().split('/')[-1]\n",
+    "if cur_folder_name != \"Virny\":\n",
+    "    os.chdir(\"../..\")\n",
+    "\n",
+    "print('Current location: ', os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a578f2ab",
+   "metadata": {},
+   "source": [
+    "# Multiple Models Interface Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a9241de",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:11:52.701377Z",
+     "start_time": "2023-12-18T17:11:51.106232Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "from virny.datasets import LawSchoolDataset\n",
+    "from virny.custom_classes.metrics_composer import MetricsComposer\n",
+    "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "data_loader = LawSchoolDataset()\n",
+    "sensitive_attributes_dct = {'male': '0.0', 'race': 'Non-White', 'male&race': None}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:11:52.766489Z",
+     "start_time": "2023-12-18T17:11:52.704609Z"
+    }
+   },
+   "id": "d3c53c7b72ecbcd0"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
+   "source": [
+    "ROOT_DIR = os.path.join('docs', 'examples')\n",
+    "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'law_school_subgroup_metrics.csv'), header=0)\n",
+    "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n",
+    "                                     subgroup_metrics_df['Intervention_Param'].astype(str))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:11:52.791981Z",
+     "start_time": "2023-12-18T17:11:52.767057Z"
+    }
+   },
+   "id": "2aab7c79ecdee914"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                         Metric      male      race  male&race  \\\n0               Accuracy_Parity -0.024413 -0.158856  -0.162998   \n1  Aleatoric_Uncertainty_Parity -0.016769  0.317464   0.274695   \n2   Aleatoric_Uncertainty_Ratio  0.951019  2.126816   1.880052   \n3            Equalized_Odds_FNR  0.006853  0.089260   0.092334   \n4            Equalized_Odds_FPR  0.027311 -0.289259  -0.156572   \n\n                  Model_Name  \n0  LGBMClassifier__alpha=0.6  \n1  LGBMClassifier__alpha=0.6  \n2  LGBMClassifier__alpha=0.6  \n3  LGBMClassifier__alpha=0.6  \n4  LGBMClassifier__alpha=0.6  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metric</th>\n      <th>male</th>\n      <th>race</th>\n      <th>male&amp;race</th>\n      <th>Model_Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Accuracy_Parity</td>\n      <td>-0.024413</td>\n      <td>-0.158856</td>\n      <td>-0.162998</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Aleatoric_Uncertainty_Parity</td>\n      <td>-0.016769</td>\n      <td>0.317464</td>\n      <td>0.274695</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Aleatoric_Uncertainty_Ratio</td>\n      <td>0.951019</td>\n      <td>2.126816</td>\n      <td>1.880052</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Equalized_Odds_FNR</td>\n      <td>0.006853</td>\n      <td>0.089260</td>\n      <td>0.092334</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Equalized_Odds_FPR</td>\n      <td>0.027311</td>\n      <td>-0.289259</td>\n      <td>-0.156572</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_names = subgroup_metrics_df['Model_Name'].unique()\n",
+    "models_metrics_dct = dict()\n",
+    "for model_name in model_names:\n",
+    "    models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n",
+    "\n",
+    "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n",
+    "models_composed_metrics_df = metrics_composer.compose_metrics()\n",
+    "models_composed_metrics_df.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:11:52.842306Z",
+     "start_time": "2023-12-18T17:11:52.792667Z"
+    }
+   },
+   "id": "833484748ed512e8"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.6', 'LogisticRegression__alpha=0.0', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0'])"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models_metrics_dct.keys()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:11:52.877906Z",
+     "start_time": "2023-12-18T17:11:52.842425Z"
+    }
+   },
+   "id": "15ed7d1ba1f22317"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deb45226",
+   "metadata": {},
+   "source": [
+    "## Metrics Visualization and Reporting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "435b9d98",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:11:52.959909Z",
+     "start_time": "2023-12-18T17:11:52.864927Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n",
+    "                                          models_metrics_dct, models_composed_metrics_df,\n",
+    "                                          sensitive_attributes_dct=sensitive_attributes_dct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n",
+      "Keyboard interruption in main thread... closing server.\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.start_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:14:45.540473Z",
+     "start_time": "2023-12-18T17:11:52.892884Z"
+    }
+   },
+   "id": "678a9dc8d51243f4"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7860\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.stop_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:14:45.583530Z",
+     "start_time": "2023-12-18T17:14:45.541605Z"
+    }
+   },
+   "id": "277b6d1de837dab7"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2326c129",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-18T17:14:45.584046Z",
+     "start_time": "2023-12-18T17:14:45.581453Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb
new file mode 100644
index 00000000..6caf5b8b
--- /dev/null
+++ b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "248cbed8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:13:42.978064Z",
+     "start_time": "2023-12-07T00:13:42.914700Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7ec6cd08",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:13:42.983725Z",
+     "start_time": "2023-12-07T00:13:42.954698Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b8cb69f2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:13:43.018895Z",
+     "start_time": "2023-12-07T00:13:42.982387Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current location:  /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n"
+     ]
+    }
+   ],
+   "source": [
+    "cur_folder_name = os.getcwd().split('/')[-1]\n",
+    "if cur_folder_name != \"Virny\":\n",
+    "    os.chdir(\"../..\")\n",
+    "\n",
+    "print('Current location: ', os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a578f2ab",
+   "metadata": {},
+   "source": [
+    "# Multiple Models Interface Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7a9241de",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:13:43.027909Z",
+     "start_time": "2023-12-07T00:13:43.006390Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "from virny.datasets import ACSPublicCoverageDataset\n",
+    "from virny.custom_classes.metrics_composer import MetricsComposer\n",
+    "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [],
+   "source": [
+    "data_loader = ACSPublicCoverageDataset(state=['CA'], year=2018, with_nulls=False,\n",
+    "                                       subsample_size=15_000, subsample_seed=42)\n",
+    "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:13:48.771709Z",
+     "start_time": "2023-12-07T00:13:43.029632Z"
+    }
+   },
+   "id": "d3c53c7b72ecbcd0"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [],
+   "source": [
+    "ROOT_DIR = os.path.join('docs', 'examples')\n",
+    "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'pub_cov_subgroup_metrics.csv'), header=0)\n",
+    "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n",
+    "                                     subgroup_metrics_df['Intervention_Param'].astype(str))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:13:48.805639Z",
+     "start_time": "2023-12-07T00:13:48.768740Z"
+    }
+   },
+   "id": "2aab7c79ecdee914"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                         Metric       SEX     RAC1P  SEX&RAC1P  \\\n0               Accuracy_Parity  0.026847  0.016299   0.040212   \n1  Aleatoric_Uncertainty_Parity -0.013240  0.027276   0.007235   \n2   Aleatoric_Uncertainty_Ratio  0.983584  1.034689   1.009077   \n3            Equalized_Odds_FNR  0.004275 -0.000359  -0.008617   \n4            Equalized_Odds_FPR -0.012072 -0.024172  -0.040481   \n\n                  Model_Name  \n0  LGBMClassifier__alpha=0.6  \n1  LGBMClassifier__alpha=0.6  \n2  LGBMClassifier__alpha=0.6  \n3  LGBMClassifier__alpha=0.6  \n4  LGBMClassifier__alpha=0.6  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metric</th>\n      <th>SEX</th>\n      <th>RAC1P</th>\n      <th>SEX&amp;RAC1P</th>\n      <th>Model_Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Accuracy_Parity</td>\n      <td>0.026847</td>\n      <td>0.016299</td>\n      <td>0.040212</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Aleatoric_Uncertainty_Parity</td>\n      <td>-0.013240</td>\n      <td>0.027276</td>\n      <td>0.007235</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Aleatoric_Uncertainty_Ratio</td>\n      <td>0.983584</td>\n      <td>1.034689</td>\n      <td>1.009077</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Equalized_Odds_FNR</td>\n      <td>0.004275</td>\n      <td>-0.000359</td>\n      <td>-0.008617</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Equalized_Odds_FPR</td>\n      <td>-0.012072</td>\n      <td>-0.024172</td>\n      <td>-0.040481</td>\n      <td>LGBMClassifier__alpha=0.6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_names = subgroup_metrics_df['Model_Name'].unique()\n",
+    "models_metrics_dct = dict()\n",
+    "for model_name in model_names:\n",
+    "    models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n",
+    "\n",
+    "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n",
+    "models_composed_metrics_df = metrics_composer.compose_metrics()\n",
+    "models_composed_metrics_df.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:13:48.849022Z",
+     "start_time": "2023-12-07T00:13:48.802693Z"
+    }
+   },
+   "id": "833484748ed512e8"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.6', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.6'])"
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models_metrics_dct.keys()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:13:48.873723Z",
+     "start_time": "2023-12-07T00:13:48.848261Z"
+    }
+   },
+   "id": "15ed7d1ba1f22317"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deb45226",
+   "metadata": {},
+   "source": [
+    "## Metrics Visualization and Reporting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "435b9d98",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:13:48.959344Z",
+     "start_time": "2023-12-07T00:13:48.871083Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n",
+    "                                          models_metrics_dct, models_composed_metrics_df,\n",
+    "                                          sensitive_attributes_dct=sensitive_attributes_dct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n",
+      "Keyboard interruption in main thread... closing server.\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.start_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:15:48.056146Z",
+     "start_time": "2023-12-07T00:13:48.898642Z"
+    }
+   },
+   "id": "678a9dc8d51243f4"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7860\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.stop_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:15:48.092702Z",
+     "start_time": "2023-12-07T00:15:48.056394Z"
+    }
+   },
+   "id": "277b6d1de837dab7"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "2326c129",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-07T00:15:48.095103Z",
+     "start_time": "2023-12-07T00:15:48.092153Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb
new file mode 100644
index 00000000..8e21b6bc
--- /dev/null
+++ b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb
@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "248cbed8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.642940Z",
+     "start_time": "2023-10-07T13:42:22.508015Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "7ec6cd08",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.677119Z",
+     "start_time": "2023-10-07T13:42:22.641937Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "b8cb69f2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.689334Z",
+     "start_time": "2023-10-07T13:42:22.664188Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current location:  /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n"
+     ]
+    }
+   ],
+   "source": [
+    "cur_folder_name = os.getcwd().split('/')[-1]\n",
+    "if cur_folder_name != \"Virny\":\n",
+    "    os.chdir(\"../..\")\n",
+    "\n",
+    "print('Current location: ', os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a578f2ab",
+   "metadata": {},
+   "source": [
+    "# Multiple Models Interface Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "7a9241de",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.711038Z",
+     "start_time": "2023-10-07T13:42:22.687552Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "outputs": [],
+   "source": [
+    "sensitive_attributes_dct = {'Race': 'Non-White'}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.732136Z",
+     "start_time": "2023-10-07T13:42:22.711244Z"
+    }
+   },
+   "id": "d3c53c7b72ecbcd0"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "outputs": [],
+   "source": [
+    "ROOT_DIR = os.path.join('docs', 'examples')\n",
+    "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'ricci_subgroup_metrics.csv'), header=0)\n",
+    "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'ricci_group_metrics.csv'), header=0)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.759203Z",
+     "start_time": "2023-10-07T13:42:22.732607Z"
+    }
+   },
+   "id": "2aab7c79ecdee914"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "outputs": [],
+   "source": [
+    "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n",
+    "                                     subgroup_metrics_df['Intervention_Param'].astype(str))\n",
+    "models_composed_metrics_df['Model_Name'] = (models_composed_metrics_df['Model_Name'] + '__alpha=' \n",
+    "                                            + models_composed_metrics_df['Intervention_Param'].astype(str))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.784062Z",
+     "start_time": "2023-10-07T13:42:22.759791Z"
+    }
+   },
+   "id": "2d922003e752a4b4"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "outputs": [],
+   "source": [
+    "models_metrics_dct = dict()\n",
+    "for model_name in subgroup_metrics_df['Model_Name'].unique():\n",
+    "    models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.809161Z",
+     "start_time": "2023-10-07T13:42:22.782462Z"
+    }
+   },
+   "id": "833484748ed512e8"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "dict_keys(['LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LGBMClassifier__alpha=0.7', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.4', 'LogisticRegression__alpha=0.7', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7'])"
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models_metrics_dct.keys()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.831140Z",
+     "start_time": "2023-10-07T13:42:22.806994Z"
+    }
+   },
+   "id": "15ed7d1ba1f22317"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deb45226",
+   "metadata": {},
+   "source": [
+    "## Metrics Visualization and Reporting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "435b9d98",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:42:22.859150Z",
+     "start_time": "2023-10-07T13:42:22.830292Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n",
+    "                                          sensitive_attributes_dct=sensitive_attributes_dct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n",
+      "Keyboard interruption in main thread... closing server.\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.start_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:45:45.222662Z",
+     "start_time": "2023-10-07T13:42:22.859325Z"
+    }
+   },
+   "id": "678a9dc8d51243f4"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7860\n"
+     ]
+    }
+   ],
+   "source": [
+    "visualizer.stop_web_app()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:45:45.264959Z",
+     "start_time": "2023-10-07T13:45:45.221841Z"
+    }
+   },
+   "id": "277b6d1de837dab7"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "2326c129",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-07T13:45:45.265758Z",
+     "start_time": "2023-10-07T13:45:45.264074Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/income_group_metrics.csv b/docs/examples/income_group_metrics.csv
new file mode 100644
index 00000000..25b06763
--- /dev/null
+++ b/docs/examples/income_group_metrics.csv
@@ -0,0 +1,133 @@
+Metric,SEX,RAC1P,SEX&RAC1P,Model_Name,Intervention_Param
+Equalized_Odds_TPR,-0.03079268292682924,0.11074514666563329,0.05249773566501781,LGBMClassifier,0.7
+Equalized_Odds_FPR,-0.02131701139721401,0.0009518370454978109,-0.00700793796337533,LGBMClassifier,0.7
+Equalized_Odds_FNR,0.030792682926829296,-0.11074514666563334,-0.05249773566501781,LGBMClassifier,0.7
+Disparate_Impact,1.0451749734888653,1.3342133960856337,1.248406706539857,LGBMClassifier,0.7
+Statistical_Parity_Difference,0.03246951219512195,0.22563645522532638,0.17651362084581623,LGBMClassifier,0.7
+Accuracy_Parity,0.04775641025641031,0.07497732132443469,0.0652173913043479,LGBMClassifier,0.7
+Label_Stability_Ratio,1.0095819811577007,1.0301209785116932,1.012842085178694,LGBMClassifier,0.7
+IQR_Parity,-0.0026551143311698278,-0.00967660527132716,-0.005313076583927184,LGBMClassifier,0.7
+Std_Parity,-0.002214117425894342,-0.00706110127509476,-0.004207550960833459,LGBMClassifier,0.7
+Std_Ratio,0.9581473862978338,0.8695701641666075,0.9199532195084829,LGBMClassifier,0.7
+Jitter_Parity,-0.007536566378903806,-0.019030010009223178,-0.009410112766584558,LGBMClassifier,0.7
+Equalized_Odds_TPR,-0.01097560975609757,-0.00598674778160968,-0.05549362502612687,LGBMClassifier,0.0
+Equalized_Odds_FPR,-0.025116082735331363,-0.014481520763645256,-0.019877655812003875,LGBMClassifier,0.0
+Equalized_Odds_FNR,0.01097560975609757,0.005986747781609625,0.055493625026126925,LGBMClassifier,0.0
+Disparate_Impact,1.0739042728773152,1.1095233662260287,1.0641057210867602,LGBMClassifier,0.0
+Statistical_Parity_Difference,0.061432926829268264,0.09117681249273446,0.05441371141921547,LGBMClassifier,0.0
+Accuracy_Parity,0.04294871794871791,0.037355018466921575,0.032355915065722995,LGBMClassifier,0.0
+Label_Stability_Ratio,1.013086132198451,1.0203793128013074,1.0132200761605896,LGBMClassifier,0.0
+IQR_Parity,-0.0030377716953829265,-0.007959024854970922,-0.005539567672418727,LGBMClassifier,0.0
+Std_Parity,-0.0021021596178428525,-0.005701112525753914,-0.0038189830524456084,LGBMClassifier,0.0
+Std_Ratio,0.9643024235570372,0.9045579272645509,0.9347582189106831,LGBMClassifier,0.0
+Jitter_Parity,-0.008137675557275542,-0.013465396867056778,-0.009009568683773159,LGBMClassifier,0.0
+Equalized_Odds_TPR,-0.039253048780487854,-0.031483705971248166,-0.11670034139204344,LGBMClassifier,0.4
+Equalized_Odds_FPR,-0.00873786407766991,-0.013488890701911838,-0.010826281732205081,LGBMClassifier,0.4
+Equalized_Odds_FNR,0.039253048780487854,0.03148370597124811,0.1167003413920435,LGBMClassifier,0.4
+Disparate_Impact,1.0594573415613675,1.0609451814521111,0.9916513681265844,LGBMClassifier,0.4
+Statistical_Parity_Difference,0.046265243902439024,0.04779710931142722,-0.00665366125548672,LGBMClassifier,0.4
+Accuracy_Parity,0.02841880341880343,0.034390591589451236,0.015897090214582632,LGBMClassifier,0.4
+Label_Stability_Ratio,1.0214945785801577,1.021203066210909,1.0204956799007319,LGBMClassifier,0.4
+IQR_Parity,-0.004103223054099589,-0.008620361682032895,-0.006324316490461224,LGBMClassifier,0.4
+Std_Parity,-0.0030876402128535846,-0.006194968170589871,-0.004796370058731313,LGBMClassifier,0.4
+Std_Ratio,0.9493557726215703,0.8992698436233884,0.9204716301184785,LGBMClassifier,0.4
+Jitter_Parity,-0.01200876712622645,-0.015122226339372366,-0.012784745357312319,LGBMClassifier,0.4
+Equalized_Odds_TPR,-0.06996951219512193,-0.01604215910411899,-0.10732947815787641,LogisticRegression,0.0
+Equalized_Odds_FPR,-0.023723089911355008,-0.04367572271627097,-0.042787718424982316,LogisticRegression,0.0
+Equalized_Odds_FNR,0.06996951219512193,0.01604215910411899,0.10732947815787636,LogisticRegression,0.0
+Disparate_Impact,0.9908320359799343,0.9996470017208666,0.9084844203751667,LogisticRegression,0.0
+Statistical_Parity_Difference,-0.008079268292682906,-0.00030999341263993063,-0.08123737197798375,LogisticRegression,0.0
+Accuracy_Parity,0.01816239316239321,0.05037905786302077,0.031457139647230625,LogisticRegression,0.0
+Label_Stability_Ratio,1.009100642398287,1.023801267627326,1.0076597165218943,LogisticRegression,0.0
+IQR_Parity,-0.006097632012806539,-0.006310527017404574,-0.0065692439650302525,LogisticRegression,0.0
+Std_Parity,-0.004906109963734577,-0.004854434742104861,-0.005207661750716387,LogisticRegression,0.0
+Std_Ratio,0.9086406912235572,0.9082781978742059,0.9003166552931693,LogisticRegression,0.0
+Jitter_Parity,-0.0055498432332622555,-0.01576926661754173,-0.006298770939673112,LogisticRegression,0.0
+Equalized_Odds_TPR,-0.04298780487804876,0.08555818188863484,0.017661812861422654,LogisticRegression,0.7
+Equalized_Odds_FPR,-0.003925707049387925,-0.030295613390987464,-0.022281313038694295,LogisticRegression,0.7
+Equalized_Odds_FNR,0.04298780487804876,-0.0855581818886349,-0.01766181286142271,LogisticRegression,0.7
+Disparate_Impact,1.0570386018820819,1.15227520571032,1.1070776454221372,LogisticRegression,0.7
+Statistical_Parity_Difference,0.04527439024390245,0.11903747045375279,0.08580087786525459,LogisticRegression,0.7
+Accuracy_Parity,0.02147435897435901,0.07841152076718727,0.05596749428903114,LogisticRegression,0.7
+Label_Stability_Ratio,0.9984503821387735,1.0050781309776278,0.9984788609152078,LogisticRegression,0.7
+IQR_Parity,-0.0030463198184801366,-0.0007317941412861503,-0.0023944641804607703,LogisticRegression,0.7
+Std_Parity,-0.002576978265789877,-0.00016051358574650093,-0.0019729774914916606,LogisticRegression,0.7
+Std_Ratio,0.9398193735885796,0.9961448364837571,0.9529382123765405,LogisticRegression,0.7
+Jitter_Parity,0.0017610005153971056,-0.0049119190685485425,0.0009849212357710413,LogisticRegression,0.7
+Equalized_Odds_TPR,-0.062347560975609784,-0.0033518037741697704,-0.09653034208876188,LogisticRegression,0.4
+Equalized_Odds_FPR,-0.019607429295061207,-0.030417992439694327,-0.030016242697335674,LogisticRegression,0.4
+Equalized_Odds_FNR,0.06234756097560973,0.0033518037741697704,0.09653034208876193,LogisticRegression,0.4
+Disparate_Impact,1.0087170907810161,1.050002258457925,0.961194506547429,LogisticRegression,0.4
+Statistical_Parity_Difference,0.007545731707317094,0.042895338474057354,-0.0338605169650944,LogisticRegression,0.4
+Accuracy_Parity,0.019230769230769273,0.046207801464394516,0.026120660599932566,LogisticRegression,0.4
+Label_Stability_Ratio,1.0072028640298956,1.0153655245856517,1.000428717824364,LogisticRegression,0.4
+IQR_Parity,-0.0068586087713478905,-0.005069227313861113,-0.005323993156258602,LogisticRegression,0.4
+Std_Parity,-0.005429382866877996,-0.004096338358900525,-0.004725493931369902,LogisticRegression,0.4
+Std_Ratio,0.8999072809699203,0.9226669785897548,0.909905157452225,LogisticRegression,0.4
+Jitter_Parity,-0.004078888029894576,-0.01278422827543911,-0.003752072160978573,LogisticRegression,0.4
+Equalized_Odds_TPR,-0.019435975609756184,-0.03632735304374779,-0.07838082630808896,MLPClassifier,0.0
+Equalized_Odds_FPR,-0.0587378640776699,-0.05070571918087624,-0.06589426527992455,MLPClassifier,0.0
+Equalized_Odds_FNR,0.019435975609756073,0.036327353043747845,0.0783808263080889,MLPClassifier,0.0
+Disparate_Impact,0.9938211382113822,0.9835774706003713,0.9036308734717926,MLPClassifier,0.0
+Statistical_Parity_Difference,-0.005792682926829218,-0.01542217227883913,-0.09116560997700829,MLPClassifier,0.0
+Accuracy_Parity,0.05256410256410249,0.04415862113652558,0.05033142343556907,MLPClassifier,0.0
+Label_Stability_Ratio,1.0400222540384076,1.052024430659532,1.0377142721189696,MLPClassifier,0.0
+IQR_Parity,-0.023103483282492315,-0.02199865172956933,-0.021084662231184464,MLPClassifier,0.0
+Std_Parity,-0.017047311682108574,-0.014482498155605067,-0.015528142059770114,MLPClassifier,0.0
+Std_Ratio,0.8378767214807724,0.8575668018548829,0.8441550952374669,MLPClassifier,0.0
+Jitter_Parity,-0.024276623502126712,-0.02999195460552799,-0.02431443345626763,MLPClassifier,0.0
+Equalized_Odds_TPR,-0.018978658536585313,0.05504320533188678,-0.03720476555423957,MLPClassifier,0.7
+Equalized_Odds_FPR,-0.04624314056563952,-0.015542139185771395,-0.04150402137748552,MLPClassifier,0.7
+Equalized_Odds_FNR,0.01897865853658537,-0.05504320533188667,0.037204765554239516,MLPClassifier,0.7
+Disparate_Impact,0.9976159911975059,1.1685710118150436,0.9892333472978635,MLPClassifier,0.7
+Statistical_Parity_Difference,-0.0019817073170732558,0.134343395202852,-0.008952832160523894,MLPClassifier,0.7
+Accuracy_Parity,0.05566239316239319,0.05791971748849867,0.05465678013706321,MLPClassifier,0.7
+Label_Stability_Ratio,1.0116825655056816,1.0147414387548623,1.0142450823394442,MLPClassifier,0.7
+IQR_Parity,-0.015967185873185546,-0.007739632168905608,-0.010375957070957897,MLPClassifier,0.7
+Std_Parity,-0.011280373369031718,-0.0052215839220306065,-0.00846586522301801,MLPClassifier,0.7
+Std_Ratio,0.8633086261228244,0.9337434540716136,0.8922451079710022,MLPClassifier,0.7
+Jitter_Parity,-0.011206411330150795,-0.010024072689631416,-0.009159063489553684,MLPClassifier,0.7
+Equalized_Odds_TPR,-0.018064024390243905,-0.018541480993528814,-0.07190134466662024,MLPClassifier,0.4
+Equalized_Odds_FPR,-0.0478682988602786,-0.03377661744309374,-0.04670430431479396,MLPClassifier,0.4
+Equalized_Odds_FNR,0.018064024390243905,0.01854148099352887,0.07190134466662024,MLPClassifier,0.4
+Disparate_Impact,1.0122367435278448,1.0388483701545854,0.9596397347000302,MLPClassifier,0.4
+Statistical_Parity_Difference,0.01120426829268295,0.03539737280582789,-0.03730927332265033,MLPClassifier,0.4
+Accuracy_Parity,0.04732905982905977,0.03914501393118641,0.039358873534808825,MLPClassifier,0.4
+Label_Stability_Ratio,1.0380533656761675,1.050281055097938,1.039143933855558,MLPClassifier,0.4
+IQR_Parity,-0.021800317105360498,-0.01979599811428892,-0.019109923987836203,MLPClassifier,0.4
+Std_Parity,-0.016632398891789663,-0.013618988897571183,-0.014615438426665558,MLPClassifier,0.4
+Std_Ratio,0.8411837472975571,0.8653910518588449,0.8527524702482466,MLPClassifier,0.4
+Jitter_Parity,-0.023205391272604736,-0.028007441470945263,-0.022731439845363424,MLPClassifier,0.4
+Equalized_Odds_TPR,-0.013109756097560932,0.03212306738481807,-0.022085975057479224,RandomForestClassifier,0.4
+Equalized_Odds_FPR,-0.01192486281131279,-0.004963150308667147,-0.006942443216054084,RandomForestClassifier,0.4
+Equalized_Odds_FNR,0.013109756097560987,-0.03212306738481807,0.02208597505747928,RandomForestClassifier,0.4
+Disparate_Impact,1.0743556828280834,1.1590880271867312,1.1109037589976007,RandomForestClassifier,0.4
+Statistical_Parity_Difference,0.05739329268292681,0.12153679234316273,0.08695046331777334,RandomForestClassifier,0.4
+Accuracy_Parity,0.03856837606837615,0.04690436078533011,0.036587649327790794,RandomForestClassifier,0.4
+Label_Stability_Ratio,1.0010830866898295,1.0245213649857032,1.0016860934867435,RandomForestClassifier,0.4
+IQR_Parity,-0.0024315475130579356,-0.007422421027040563,-0.006348073239051066,RandomForestClassifier,0.4
+Std_Parity,-0.001890817887290966,-0.006005398383778858,-0.005054462180121233,RandomForestClassifier,0.4
+Std_Ratio,0.9693944783088382,0.9044128961451383,0.9181358364959677,RandomForestClassifier,0.4
+Jitter_Parity,-0.0029367081132166684,-0.015830939834926464,-0.004035590076358689,RandomForestClassifier,0.4
+Equalized_Odds_TPR,-0.013262195121951259,0.09049870190258458,0.0275900508604473,RandomForestClassifier,0.7
+Equalized_Odds_FPR,-0.004706627268889818,0.005180713061923803,0.004610830211417033,RandomForestClassifier,0.7
+Equalized_Odds_FNR,0.013262195121951204,-0.09049870190258458,-0.0275900508604473,RandomForestClassifier,0.7
+Disparate_Impact,1.086494036746535,1.2810291207237774,1.2183023097164019,RandomForestClassifier,0.7
+Statistical_Parity_Difference,0.06135670731707321,0.19258340760258852,0.15606493416010592,RandomForestClassifier,0.7
+Accuracy_Parity,0.04049145299145296,0.06377567550055074,0.04785979103471516,RandomForestClassifier,0.7
+Label_Stability_Ratio,1.0042931659613945,1.0158033089241028,0.997701913713356,RandomForestClassifier,0.7
+IQR_Parity,-0.0021576183268891685,-0.0011512403501528212,-0.00012869147334378106,RandomForestClassifier,0.7
+Std_Parity,-0.002107574597948185,-0.0019021823908419097,-0.001451921355860343,RandomForestClassifier,0.7
+Std_Ratio,0.962673667602888,0.9660752793094682,0.9739339258916726,RandomForestClassifier,0.7
+Jitter_Parity,-0.004579489326979741,-0.010039410100458009,-0.0014495632866055874,RandomForestClassifier,0.7
+Equalized_Odds_TPR,-0.006478658536585358,0.034680513039097915,-0.010276597227060535,RandomForestClassifier,0.0
+Equalized_Odds_FPR,-0.018446601941747576,-0.00981751924070598,-0.010793534358544452,RandomForestClassifier,0.0
+Equalized_Odds_FNR,0.006478658536585358,-0.034680513039097915,0.010276597227060535,RandomForestClassifier,0.0
+Disparate_Impact,1.0696725293946165,1.149977548271217,1.1161027349228612,RandomForestClassifier,0.0
+Statistical_Parity_Difference,0.05464939024390236,0.11648002479947306,0.09228035950672331,RandomForestClassifier,0.0
+Accuracy_Parity,0.043910256410256476,0.04994168340568905,0.04126877129910489,RandomForestClassifier,0.0
+Label_Stability_Ratio,1.0018200544605445,1.031331519636685,1.0152022947420831,RandomForestClassifier,0.0
+IQR_Parity,-0.0014266839924084312,-0.005259735864872772,-0.003978617177466615,RandomForestClassifier,0.0
+Std_Parity,-0.0014799865759114808,-0.0045115073360025085,-0.003718279422753004,RandomForestClassifier,0.0
+Std_Ratio,0.9712188827286748,0.913538681205104,0.9275913116942456,RandomForestClassifier,0.0
+Jitter_Parity,-0.0018609822617384336,-0.017820314313740024,-0.008520772043575799,RandomForestClassifier,0.0
diff --git a/docs/examples/income_subgroup_metrics.csv b/docs/examples/income_subgroup_metrics.csv
new file mode 100644
index 00000000..f9e45d09
--- /dev/null
+++ b/docs/examples/income_subgroup_metrics.csv
@@ -0,0 +1,221 @@
+Metric,Model_Name,Model_Params,Dataset_Name,Intervention_Param,RAC1P_dis,RAC1P_priv,SEX&RAC1P_dis,SEX&RAC1P_priv,SEX_dis,SEX_priv,overall
+Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.8299180327868853,0.7549407114624506,0.8333333333333334,0.7681159420289855,0.8041666666666667,0.7564102564102564,0.7793333333333333
+Aleatoric_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.574768407248688,0.5867152963701037,0.5751925521025802,0.5844148021106594,0.5625457885372674,0.6015511473295796,0.5828285751092698
+F1,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.6666666666666666,0.6242424242424243,0.6324786324786325,0.6363636363636364,0.6072423398328691,0.6545454545454545,0.6358635863586358
+FNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.366412213740458,0.47715736040609136,0.4032258064516129,0.4557235421166307,0.4682926829268293,0.4375,0.44952380952380955
+FPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.09803921568627451,0.0970873786407767,0.09183673469387756,0.09884467265725289,0.08737864077669903,0.10869565217391304,0.09743589743589744
+IQR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.06280707944938806,0.07248368472071522,0.06493633506095174,0.07024941164487893,0.06795490302023513,0.07061001735140496,0.06933556247244345
+Jitter,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.05725214185682598,0.07628215186604916,0.062299481905650766,0.07170959467223532,0.0661720407593532,0.07370860713825701,0.07009105527638122
+Label_Stability,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.9190368852459017,0.8921640316205534,0.9104651162790697,0.8989210950080515,0.905375,0.8967820512820512,0.9009066666666667
+Mean,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.7085616306769631,0.6862178585472839,0.7245281799737864,0.6870388713260648,0.7252005764269102,0.6642129917856846,0.6934870324134728
+Overall_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.5874845827200873,0.6027417432630668,0.5884432399246448,0.5997172018108436,0.5773910073017967,0.616596917041452,0.5977780803664176
+PPV,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.7033898305084746,0.7744360902255639,0.6727272727272727,0.7659574468085106,0.7077922077922078,0.782608695652174,0.7526041666666666
+Per_Sample_Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.828125,0.7573962450592885,0.8262015503875969,0.7708937198067634,0.8038958333333333,0.7587243589743589,0.7804066666666668
+Positive-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.9007633587786259,0.6751269035532995,0.8870967741935484,0.7105831533477321,0.751219512195122,0.71875,0.7314285714285714
+Sample_Size,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0
+Selection-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.24180327868852458,0.2628458498023715,0.2131782945736434,0.2648953301127214,0.21388888888888888,0.2948717948717949,0.256
+Std,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.04707606166754985,0.05413716294264461,0.04835609913224369,0.05256365009307715,0.05068861026634873,0.05290272769224307,0.051839951327813785
+TNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.9019607843137255,0.9029126213592233,0.9081632653061225,0.9011553273427471,0.912621359223301,0.8913043478260869,0.9025641025641026
+TPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.6335877862595419,0.5228426395939086,0.5967741935483871,0.5442764578833693,0.5317073170731708,0.5625,0.5504761904761905
+Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.8278688524590164,0.7905138339920948,0.8294573643410853,0.7971014492753623,0.825,0.782051282051282,0.8026666666666666
+Aleatoric_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.5633808779503379,0.6053039361523177,0.5635466006397141,0.5975059813694559,0.5732441010754783,0.6086688449379051,0.5916649678839402
+F1,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.6666666666666666,0.7063711911357341,0.6271186440677966,0.705607476635514,0.6752577319587629,0.7098976109215017,0.6960985626283368
+FNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.35877862595419846,0.35279187817258884,0.4032258064516129,0.34773218142548595,0.36097560975609755,0.35,0.35428571428571426
+FPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.10364145658263306,0.11812297734627832,0.09693877551020408,0.11681643132220795,0.10097087378640776,0.12608695652173912,0.11282051282051282
+IQR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.0717782678051346,0.07973729266010553,0.0725611945411923,0.07810076221361102,0.07556831529235587,0.0786060869877388,0.077147956573955
+Jitter,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.06448780789191826,0.07795320475897503,0.06611253944139346,0.07512210812516662,0.06934087102177533,0.07747854657905087,0.07357246231155734
+Label_Stability,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.9095491803278689,0.8913833992094861,0.907093023255814,0.895257648953301,0.9033611111111111,0.8916923076923077,0.8972933333333334
+Mean,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.6957755997986098,0.6346221249287431,0.7150553202294089,0.6419418764174092,0.689198775103901,0.6225038013523088,0.6545173887530731
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+PPV,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.6942148760330579,0.7774390243902439,0.6607142857142857,0.7684478371501272,0.7158469945355191,0.7819548872180451,0.755011135857461
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+Positive-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.9236641221374046,0.8324873096446701,0.9032258064516129,0.8488120950323974,0.8926829268292683,0.83125,0.8552380952380952
+Sample_Size,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0
+Selection-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.24795081967213115,0.3241106719367589,0.21705426356589147,0.3164251207729469,0.25416666666666665,0.34102564102564104,0.29933333333333334
+Std,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.05403263342459364,0.059733745950347555,0.05471686604121065,0.05853584909365626,0.05678586100735733,0.05888802062520018,0.057878984008635614
+TNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.896358543417367,0.8818770226537217,0.9030612244897959,0.883183568677792,0.8990291262135922,0.8739130434782608,0.8871794871794871
+TPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.6412213740458015,0.6472081218274112,0.5967741935483871,0.652267818574514,0.6390243902439025,0.65,0.6457142857142857
+Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.8278688524590164,0.7934782608695652,0.8178294573643411,0.8019323671497585,0.8194444444444444,0.791025641025641,0.8046666666666666
+Aleatoric_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.5551920500099683,0.6160978769562332,0.5546274648036941,0.6049363010992105,0.5746912318145948,0.6162142115103388,0.5962831812563817
+F1,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.65,0.7027027027027027,0.5765765765765766,0.7043269230769231,0.6524064171122995,0.7135325131810193,0.689289501590668
+FNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.40458015267175573,0.3730964467005076,0.4838709677419355,0.367170626349892,0.40487804878048783,0.365625,0.38095238095238093
+FPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.08683473389355742,0.10032362459546926,0.08673469387755102,0.0975609756097561,0.0912621359223301,0.1,0.09538461538461539
+IQR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.07359398945751415,0.08221435113954705,0.07417332608489044,0.08049764257535166,0.07727618415086056,0.08137940720496015,0.07940986013899233
+Jitter,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.06965174231814622,0.08477396865751859,0.06926843519925442,0.08205318055656674,0.07360964544947027,0.08561841257569672,0.07985420435511108
+Label_Stability,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.8993852459016393,0.8807114624505928,0.9017829457364341,0.883671497584541,0.8965972222222222,0.8777307692307692,0.8867866666666667
+Mean,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.7141488205426961,0.6441152206909166,0.7326595827361654,0.6532391750548413,0.6999423689094346,0.6363983617298083,0.6668994851760289
+Overall_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.5729327590451901,0.6355678274191867,0.5723997447559693,0.624079463458317,0.5933199449151484,0.6353788043889269,0.6151905518415132
+PPV,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.7155963302752294,0.7993527508090615,0.6530612244897959,0.7940379403794038,0.7218934911242604,0.8152610441767069,0.777511961722488
+Per_Sample_Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.8241188524590164,0.7855237154150199,0.8176744186046511,0.7940096618357488,0.8138402777777777,0.7835320512820513,0.7980799999999999
+Positive-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.8320610687022901,0.7842639593908629,0.7903225806451613,0.796976241900648,0.824390243902439,0.778125,0.7961904761904762
+Sample_Size,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0
+Selection-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.22336065573770492,0.30533596837944665,0.18992248062015504,0.2971014492753623,0.23472222222222222,0.3192307692307692,0.2786666666666667
+Statistical_Bias,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.2485877124816216,0.2850127848791134,0.2544548274710566,0.2770486284228354,0.25981461969725383,0.28548361000855305,0.2731624946591294
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+TNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.9131652661064426,0.8996763754045307,0.9132653061224489,0.9024390243902439,0.9087378640776699,0.9,0.9046153846153846
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+PPV,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.788135593220339,0.7987012987012987,0.7454545454545455,0.8032345013477089,0.7383720930232558,0.8346456692913385,0.795774647887324
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+Positive-Rate,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.9007633587786259,0.7817258883248731,0.8870967741935484,0.8012958963282938,0.8390243902439024,0.79375,0.8114285714285714
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+Accuracy,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8545081967213115,0.808300395256917,0.8449612403100775,0.8188405797101449,0.8333333333333334,0.8141025641025641,0.8233333333333334
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+PPV,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.7542372881355932,0.7958579881656804,0.7115384615384616,0.7945544554455446,0.7374301675977654,0.8158844765342961,0.7850877192982456
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+Positive-Rate,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.9007633587786259,0.8578680203045685,0.8387096774193549,0.8725701943844493,0.8731707317073171,0.865625,0.8685714285714285
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+Std,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.048873767867387855,0.05297010622628838,0.0477247218383518,0.052450215769721704,0.04881415172274951,0.05424353458962751,0.051637430813526085
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+FPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.08683473389355742,0.13754045307443366,0.0663265306122449,0.13222079589216945,0.0912621359223301,0.15,0.11897435897435897
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+Mean,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7252847058872565,0.6240034562362897,0.7607214461032801,0.6353979879947343,0.7097566779513825,0.6082123411014243,0.6569536227894043
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+Selection-Rate,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.24795081967213115,0.36561264822134387,0.2054263565891473,0.3526570048309179,0.2652777777777778,0.38461538461538464,0.3273333333333333
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+FPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.0896358543417367,0.10517799352750809,0.0663265306122449,0.10783055198973042,0.07766990291262135,0.12391304347826088,0.09948717948717949
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+Mean,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.7102203181759992,0.6607804033163299,0.7506239522883105,0.6615429176615372,0.7249338287368677,0.6324934958147033,0.6768648556173422
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+TPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.6870229007633588,0.631979695431472,0.6129032258064516,0.6501079913606912,0.6341463414634146,0.653125,0.6457142857142857
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+F1,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.7137254901960784,0.7463479415670651,0.6837606837606838,0.745230078563412,0.7240506329113924,0.7471451876019576,0.7380952380952381
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+FPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.09243697478991597,0.1262135922330097,0.07653061224489796,0.12323491655969192,0.0912621359223301,0.1391304347826087,0.11384615384615385
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+Positive-Rate,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.9465648854961832,0.9111675126903553,0.8870967741935484,0.9244060475161987,0.926829268292683,0.915625,0.92
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+Selection-Rate,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.2540983606557377,0.3547430830039526,0.2131782945736434,0.3446054750402576,0.2638888888888889,0.37564102564102564,0.322
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+TPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.6946564885496184,0.7131979695431472,0.6451612903225806,0.7170626349892009,0.697560975609756,0.715625,0.7085714285714285
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+Aleatoric_Uncertainty,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.578901881115347,0.6427220196011102,0.5776986374569397,0.631153424602836,0.6096726402021011,0.633300642147564,0.6219592012137418
+F1,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.6963562753036437,0.7079136690647482,0.6551724137931034,0.711864406779661,0.6826666666666666,0.7195767195767195,0.7048832271762208
+FNR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.3435114503816794,0.3756345177664975,0.3870967741935484,0.3650107991360691,0.375609756097561,0.3625,0.3676190476190476
+FPR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.08403361344537816,0.0889967637540453,0.08163265306122448,0.08857509627727857,0.08155339805825243,0.09347826086956522,0.08717948717948718
+IQR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.07993080357142858,0.08735322459846914,0.07968225898240433,0.0860303322214554,0.08367405891754848,0.08610560643060641,0.0849384636243386
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+Label_Stability,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.911844262295082,0.8900197628458498,0.8983720930232557,0.8968599033816426,0.8976249999999999,0.8966538461538462,0.8971199999999999
+Mean,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.707915635896435,0.645445064778217,0.7366042713332103,0.6510542140652557,0.7011054430941357,0.6331504062118437,0.6657688239153439
+Overall_Uncertainty,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.5980926620208493,0.6629771995525174,0.5967855512878455,0.6512330698720273,0.6293954388029462,0.6533813193271806,0.6418680966755481
+PPV,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.7413793103448276,0.8172757475083057,0.7037037037037037,0.8099173553719008,0.7529411764705882,0.8259109311740891,0.7961630695443646
+Per_Sample_Accuracy,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8352459016393443,0.7941007905138341,0.8271317829457364,0.8034057971014492,0.8267013888888889,0.78975,0.8074866666666667
+Positive-Rate,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8854961832061069,0.7639593908629442,0.8709677419354839,0.7840172786177105,0.8292682926829268,0.771875,0.7942857142857143
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+Selection-Rate,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.23770491803278687,0.2974308300395257,0.20930232558139536,0.2922705314009662,0.2361111111111111,0.31666666666666665,0.278
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+Overall_Uncertainty,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.5984611689436439,0.6487402421227054,0.5980162595875916,0.6395217234292089,0.6192559619409344,0.6444998498400043,0.6323827836484507
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+Sample_Size,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0
+Selection-Rate,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.23565573770491804,0.26679841897233203,0.20930232558139536,0.2665056360708535,0.21944444444444444,0.29102564102564105,0.25666666666666665
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+TPR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.6564885496183206,0.565989847715736,0.6129032258064516,0.5853131749460043,0.5804878048780487,0.59375,0.5885714285714285
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+F1,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7016129032258065,0.7085714285714285,0.6666666666666666,0.7123947051744886,0.6896551724137931,0.7180385288966725,0.7067510548523207
+FNR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.33587786259541985,0.37055837563451777,0.3709677419354839,0.36069114470842334,0.36585365853658536,0.359375,0.3619047619047619
+FPR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.08403361344537816,0.09385113268608414,0.08163265306122448,0.09242618741976893,0.08155339805825243,0.1,0.09025641025641026
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+Label_Stability,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.9285040983606557,0.900296442687747,0.9208914728682169,0.9071014492753622,0.9103333333333334,0.9086794871794872,0.9094733333333334
+Mean,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7044707097166929,0.6383597499240815,0.7315249192761425,0.6449825636808952,0.6940397580188165,0.6283245480658572,0.6598678488432778
+Overall_Uncertainty,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.6163162753250673,0.6810105385909122,0.6151568145209929,0.6692709736442993,0.6484095577353082,0.670628366465659,0.6599633382750907
+PPV,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7435897435897436,0.8104575163398693,0.7090909090909091,0.8043478260869565,0.7558139534883721,0.8167330677290837,0.7919621749408984
+Per_Sample_Accuracy,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8393954918032787,0.7940316205533596,0.8337403100775194,0.8036070853462158,0.8301944444444445,0.7890320512820512,0.80879
+Positive-Rate,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8931297709923665,0.7766497461928934,0.8870967741935484,0.7948164146868251,0.8390243902439024,0.784375,0.8057142857142857
+Sample_Size,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0
+Selection-Rate,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.23975409836065573,0.30237154150197626,0.2131782945736434,0.2962962962962963,0.2388888888888889,0.3217948717948718,0.282
+Std,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.047667980542325225,0.052179487878327734,0.04763300879630876,0.051351288219061764,0.04994215113887428,0.05142213771478576,0.05071174415834825
+TNR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.9159663865546218,0.9061488673139159,0.9183673469387755,0.9075738125802311,0.9184466019417475,0.9,0.9097435897435897
+TPR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.6641221374045801,0.6294416243654822,0.6290322580645161,0.6393088552915767,0.6341463414634146,0.640625,0.638095238095238
diff --git a/docs/examples/law_school_group_metrics.csv b/docs/examples/law_school_group_metrics.csv
new file mode 100644
index 00000000..f39a023c
--- /dev/null
+++ b/docs/examples/law_school_group_metrics.csv
@@ -0,0 +1,89 @@
+Metric,male,race,male&race,Model_Name,Experiment_Iteration,Intervention_Param,Test_Set_Index
+Equalized_Odds_TPR,-0.006852677560728049,-0.08926010463166822,-0.09233449477351918,LGBMClassifier,Exp_iter_1,0.6,0
+Equalized_Odds_FPR,0.027310924369747913,-0.2892592592592593,-0.15657230634189157,LGBMClassifier,Exp_iter_1,0.6,0
+Equalized_Odds_FNR,0.006852677560727997,0.08926010463166825,0.09233449477351917,LGBMClassifier,Exp_iter_1,0.6,0
+Disparate_Impact,1.0155706946616037,1.0637883787525366,1.064060803474484,LGBMClassifier,Exp_iter_1,0.6,0
+Statistical_Parity_Difference,0.01661276831014935,0.06794241218413566,0.06852497096399524,LGBMClassifier,Exp_iter_1,0.6,0
+Accuracy_Parity,-0.02441327723235165,-0.15885561838018636,-0.16299790356394128,LGBMClassifier,Exp_iter_1,0.6,0
+Label_Stability_Ratio,1.0022336520605963,0.9215110409144575,0.9448800911879143,LGBMClassifier,Exp_iter_1,0.6,0
+IQR_Parity,-0.0019234170135528535,0.030752485425003136,0.026171410156253943,LGBMClassifier,Exp_iter_1,0.6,0
+Std_Parity,-0.0014947483480208142,0.022654697553924172,0.019420397675297765,LGBMClassifier,Exp_iter_1,0.6,0
+Std_Ratio,0.9362288387603354,2.1935697988767453,1.91757091989168,LGBMClassifier,Exp_iter_1,0.6,0
+Jitter_Parity,-0.0009008483276642908,0.05605017735541579,0.04049042442969653,LGBMClassifier,Exp_iter_1,0.6,0
+Equalized_Odds_TPR,-0.005171382474971176,-0.11004756903064217,-0.0987224157955865,LGBMClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FPR,-0.025282526803824923,-0.4292592592592593,-0.3133640552995392,LGBMClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FNR,0.005171382474971221,0.11004756903064213,0.09872241579558652,LGBMClassifier,Exp_iter_1,0.0,0
+Disparate_Impact,1.0093278423562047,0.9912768659262348,0.9923664122137406,LGBMClassifier,Exp_iter_1,0.0,0
+Statistical_Parity_Difference,0.009888779529904745,-0.009296665118064151,-0.008130081300812941,LGBMClassifier,Exp_iter_1,0.0,0
+Accuracy_Parity,-0.015605423094904092,-0.1335709166202118,-0.11753449368631463,LGBMClassifier,Exp_iter_1,0.0,0
+Label_Stability_Ratio,0.998577907316844,0.911696818570683,0.920586307756427,LGBMClassifier,Exp_iter_1,0.0,0
+IQR_Parity,-0.0018418992707291484,0.03070162807008311,0.02640029144412168,LGBMClassifier,Exp_iter_1,0.0,0
+Std_Parity,-0.001380608364266945,0.022270950149824872,0.01912573180911198,LGBMClassifier,Exp_iter_1,0.0,0
+Std_Ratio,0.9474740574271133,2.0150416987665265,1.7940775240850562,LGBMClassifier,Exp_iter_1,0.0,0
+Jitter_Parity,0.0009153720918688296,0.06001277824710762,0.05223850775990582,LGBMClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_TPR,-0.005365607208478229,-0.07716333043811985,-0.08536585365853655,LogisticRegression,Exp_iter_1,0.6,0
+Equalized_Odds_FPR,0.0021008403361344463,-0.2592592592592593,-0.14691683124862842,LogisticRegression,Exp_iter_1,0.6,0
+Equalized_Odds_FNR,0.005365607208478207,0.07716333043811986,0.08536585365853659,LogisticRegression,Exp_iter_1,0.6,0
+Disparate_Impact,1.014185628316063,1.0865027213593523,1.0769230769230769,LogisticRegression,Exp_iter_1,0.6,0
+Statistical_Parity_Difference,0.015190042348141253,0.09213596057123241,0.0824622531939605,LogisticRegression,Exp_iter_1,0.6,0
+Accuracy_Parity,-0.020523609163160317,-0.15885561838018636,-0.16299790356394128,LogisticRegression,Exp_iter_1,0.6,0
+Label_Stability_Ratio,1.004335707649427,0.9552448804260418,0.9689971045213348,LogisticRegression,Exp_iter_1,0.6,0
+IQR_Parity,-0.0004394323534326547,0.015022317395668628,0.013321803169827343,LogisticRegression,Exp_iter_1,0.6,0
+Std_Parity,-0.00037752191542240673,0.011290698242018816,0.010010789402424683,LogisticRegression,Exp_iter_1,0.6,0
+Std_Ratio,0.9574177968749634,2.658469055430207,2.2721629361081668,LogisticRegression,Exp_iter_1,0.6,0
+Jitter_Parity,-0.002206018404318751,0.03092054469475277,0.022461201723108764,LogisticRegression,Exp_iter_1,0.6,0
+Equalized_Odds_TPR,-0.000257377560966332,-0.098576968913487,-0.07491289198606277,LogisticRegression,Exp_iter_1,0.0,0
+Equalized_Odds_FPR,0.001014198782961384,-0.3766666666666667,-0.2736449418477068,LogisticRegression,Exp_iter_1,0.0,0
+Equalized_Odds_FNR,0.00025737756096630773,0.09857696891348698,0.07491289198606271,LogisticRegression,Exp_iter_1,0.0,0
+Disparate_Impact,1.0178946069357029,1.024124924276844,1.0353452963567156,LogisticRegression,Exp_iter_1,0.0,0
+Statistical_Parity_Difference,0.019005426376910384,0.02574130930979468,0.03774680603948899,LogisticRegression,Exp_iter_1,0.0,0
+Accuracy_Parity,-0.014941561477325838,-0.1421916062753843,-0.11596216664228953,LogisticRegression,Exp_iter_1,0.0,0
+Label_Stability_Ratio,1.000802025127658,0.9591320645489642,0.9674653511862815,LogisticRegression,Exp_iter_1,0.0,0
+IQR_Parity,-0.0004508970638314215,0.01656430213503716,0.014545259937867398,LogisticRegression,Exp_iter_1,0.0,0
+Std_Parity,-0.000250712698052144,0.012597874744057873,0.011208249522494669,LogisticRegression,Exp_iter_1,0.0,0
+Std_Ratio,0.971192962518509,2.943109990424136,2.4624081283525006,LogisticRegression,Exp_iter_1,0.0,0
+Jitter_Parity,-0.0005955631359242288,0.029270523731717988,0.02275483751946553,LogisticRegression,Exp_iter_1,0.0,0
+Equalized_Odds_TPR,-0.00542995159871984,-0.10879522087785565,-0.08885017421602792,MLPClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FPR,0.05143436685018832,-0.3496296296296296,-0.21560236998025017,MLPClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FNR,0.005429951598719784,0.10879522087785565,0.08885017421602788,MLPClassifier,Exp_iter_1,0.0,0
+Disparate_Impact,1.0186228591559474,1.0137681641813756,1.0339168490153174,MLPClassifier,Exp_iter_1,0.0,0
+Statistical_Parity_Difference,0.019652444967672933,0.014621669662876036,0.03600464576074347,MLPClassifier,Exp_iter_1,0.0,0
+Accuracy_Parity,-0.024665731650303835,-0.14909346712325133,-0.13497635415143083,MLPClassifier,Exp_iter_1,0.0,0
+Label_Stability_Ratio,1.001836062082041,0.8553840569742932,0.8648947072020089,MLPClassifier,Exp_iter_1,0.0,0
+IQR_Parity,-0.0020205977060946817,0.09889952827181274,0.08378658970964548,MLPClassifier,Exp_iter_1,0.0,0
+Std_Parity,-0.0033869150678196153,0.10496476391153896,0.09394292815043398,MLPClassifier,Exp_iter_1,0.0,0
+Std_Ratio,0.9706692880546564,2.088873447566327,1.884663033993635,MLPClassifier,Exp_iter_1,0.0,0
+Jitter_Parity,-0.0014995576292047702,0.10470271374925606,0.09636066589101067,MLPClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_TPR,0.004399249792072291,-0.12046528773708765,-0.10859465737514518,MLPClassifier,Exp_iter_1,0.6,0
+Equalized_Odds_FPR,0.03441031585047816,-0.24518518518518517,-0.18981786262892253,MLPClassifier,Exp_iter_1,0.6,0
+Equalized_Odds_FNR,-0.0043992497920722975,0.1204652877370877,0.10859465737514518,MLPClassifier,Exp_iter_1,0.6,0
+Disparate_Impact,1.0262887781579368,1.0295513811087504,1.0238227146814405,MLPClassifier,Exp_iter_1,0.6,0
+Statistical_Parity_Difference,0.027283213025210973,0.03090219564910024,0.024970963995354367,MLPClassifier,Exp_iter_1,0.6,0
+Accuracy_Parity,-0.01372604020570356,-0.17549308486634285,-0.1530398322851153,MLPClassifier,Exp_iter_1,0.6,0
+Label_Stability_Ratio,1.0106622176454783,0.7347549972966689,0.7588685481341745,MLPClassifier,Exp_iter_1,0.6,0
+IQR_Parity,-0.014337453917789456,0.239717291325784,0.20712778100148252,MLPClassifier,Exp_iter_1,0.6,0
+Std_Parity,-0.0043530382426541225,0.14139945806041038,0.12248067260767251,MLPClassifier,Exp_iter_1,0.6,0
+Std_Ratio,0.9757227989415077,1.9199580092412873,1.7324037388625755,MLPClassifier,Exp_iter_1,0.6,0
+Jitter_Parity,-0.005143773566298637,0.14638050323022922,0.12861240469325352,MLPClassifier,Exp_iter_1,0.6,0
+Equalized_Odds_TPR,-0.001291654055960545,-0.09822600844325047,-0.07026713124274109,RandomForestClassifier,Exp_iter_1,0.6,0
+Equalized_Odds_FPR,0.05252100840336138,-0.23222222222222222,-0.11465876673249942,RandomForestClassifier,Exp_iter_1,0.6,0
+Equalized_Odds_FNR,0.0012916540559605519,0.09822600844325045,0.070267131242741,RandomForestClassifier,Exp_iter_1,0.6,0
+Disparate_Impact,1.023725852624935,1.0662711508746496,1.0951859956236325,RandomForestClassifier,Exp_iter_1,0.6,0
+Statistical_Parity_Difference,0.025083588129174883,0.07017189488355191,0.10104529616724744,RandomForestClassifier,Exp_iter_1,0.6,0
+Accuracy_Parity,-0.022010285179990707,-0.1732234344721404,-0.15403929598751886,RandomForestClassifier,Exp_iter_1,0.6,0
+Label_Stability_Ratio,1.001176387259295,0.8802415712253702,0.8891938218753054,RandomForestClassifier,Exp_iter_1,0.6,0
+IQR_Parity,-0.0035096712918958883,0.04988279067634181,0.0462411893925246,RandomForestClassifier,Exp_iter_1,0.6,0
+Std_Parity,-0.0023313579149804586,0.03429320329971825,0.031556199509573314,RandomForestClassifier,Exp_iter_1,0.6,0
+Std_Ratio,0.9518148424379955,1.8242881288324113,1.7054584360968619,RandomForestClassifier,Exp_iter_1,0.6,0
+Jitter_Parity,-4.495690233047994e-05,0.07727229028260862,0.07378751418836879,RandomForestClassifier,Exp_iter_1,0.6,0
+Equalized_Odds_TPR,-0.0020673614272062046,-0.11776112468943789,-0.10162601626016254,RandomForestClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FPR,0.04303100550565053,-0.39185185185185184,-0.2725477287689269,RandomForestClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FNR,0.0020673614272062393,0.11776112468943785,0.1016260162601626,RandomForestClassifier,Exp_iter_1,0.0,0
+Disparate_Impact,1.0209866123993547,0.9959582206649654,1.004931506849315,RandomForestClassifier,Exp_iter_1,0.0,0
+Statistical_Parity_Difference,0.022045103034433744,-0.004287272506918294,0.00522648083623678,RandomForestClassifier,Exp_iter_1,0.0,0
+Accuracy_Parity,-0.020729312762973406,-0.14734809269730031,-0.13025937301935542,RandomForestClassifier,Exp_iter_1,0.0,0
+Label_Stability_Ratio,0.9930186851041846,0.8737383149825507,0.8708797513120977,RandomForestClassifier,Exp_iter_1,0.0,0
+IQR_Parity,-0.001739345488546054,0.055151728119773834,0.04682910389547073,RandomForestClassifier,Exp_iter_1,0.0,0
+Std_Parity,-0.0009731829505101527,0.03857636987638614,0.03338260860983806,RandomForestClassifier,Exp_iter_1,0.0,0
+Std_Ratio,0.9772979817363154,2.072208090053585,1.8414636557441237,RandomForestClassifier,Exp_iter_1,0.0,0
+Jitter_Parity,0.0032763339840793312,0.0841594483260172,0.08485739726863116,RandomForestClassifier,Exp_iter_1,0.0,0
diff --git a/docs/examples/law_school_subgroup_metrics.csv b/docs/examples/law_school_subgroup_metrics.csv
new file mode 100644
index 00000000..3648b11d
--- /dev/null
+++ b/docs/examples/law_school_subgroup_metrics.csv
@@ -0,0 +1,153 @@
+Metric,Bootstrap_Model_Seed,Model_Name,Model_Params,Run_Number,Dataset_Name,Num_Estimators,Tag,Record_Create_Date_Time,Session_Uuid,Experiment_Iteration,Dataset_Split_Seed,Model_Init_Seed,Fair_Intervention_Params_Lst,Intervention_Param,male&race_dis,male&race_dis_correct,male&race_dis_incorrect,male&race_priv,male&race_priv_correct,male&race_priv_incorrect,male_dis,male_dis_correct,male_dis_incorrect,male_priv,male_priv_correct,male_priv_incorrect,overall,race_dis,race_dis_correct,race_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,Test_Set_Index
+Accuracy,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.75,1.0,0.0,0.9129979035639413,1.0,0.0,0.886021505376344,1.0,0.0,0.9104347826086957,1.0,0.0,0.8995192307692308,0.7672413793103449,1.0,0.0,0.9260969976905312,1.0,0.0,0
+Aleatoric_Uncertainty,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.5868292198900119,0.4895028089709125,0.8788084526473101,0.31213451114412527,0.2746251249709554,0.7057571058047387,0.32557852040339047,0.27641138563249196,0.7077834171130161,0.34234717313819124,0.29969421995747725,0.7759165127518567,0.334849650521189,0.5991994902774432,0.5141883196970904,0.8794214970052727,0.28173547948471295,0.25202902038499314,0.6539945450780771,0
+F1,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.8365019011406845,1.0,0.0,0.9534231200897868,1.0,0.0,0.9375,1.0,0.0,0.9516658845612389,1.0,0.0,0.9453880324013587,0.8468809073724007,1.0,0.0,0.9611885991510006,1.0,0.0,0
+FNR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.10569105691056911,0.0,1.0,0.013356562137049941,0.0,1.0,0.02334152334152334,0.0,1.0,0.016488845780795344,0.0,1.0,0.01951219512195122,0.0967741935483871,0.0,1.0,0.007514088916718848,0.0,1.0,0
+FPR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.6122448979591837,0.0,1.0,0.7688172043010753,0.0,1.0,0.75,0.0,1.0,0.7226890756302521,0.0,1.0,0.7361702127659574,0.57,0.0,1.0,0.8592592592592593,0.0,1.0,0
+IQR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.053677237009712865,0.04423043496352387,0.08201764314827989,0.027505826853458922,0.023814295627974335,0.06624466658113452,0.028606573479079064,0.023741376201889028,0.0664265975961035,0.030529990492631918,0.02638713954524289,0.07264227148211065,0.02967000115484146,0.055277359210661375,0.04736805932154612,0.0813487551414487,0.02452487378565824,0.02153550951083108,0.06198534485458611,0
+Jitter,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.05720170620545171,0.028317556776123513,0.1438541544934052,0.016711281775755177,0.008955396961846785,0.09810135012414363,0.019561463230124726,0.008734144021076752,0.10372854840239312,0.020462311557789017,0.011515120972579035,0.1114109381860803,0.02005952841128785,0.06673207993993159,0.03564809064047434,0.16919411874184762,0.010681902584515802,0.006069342974223462,0.06848304020100503,0
+Label_Stability,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.922674418604651,0.9623255813953489,0.8037209302325582,0.9764989517819705,0.9878645235361653,0.8572289156626507,0.973247311827957,0.9883373786407766,0.8559433962264151,0.9710782608695653,0.9843457497612227,0.8362135922330096,0.9720480769230768,0.9076724137931035,0.9531460674157304,0.7577777777777778,0.9849826789838337,0.9915897755610973,0.9021875,0
+Mean,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.25366066560450123,0.19218317287414302,0.43809314379557607,0.09430034646818729,0.07687616230366145,0.27714931523086195,0.10592175970495323,0.08043902837613173,0.30401393531767884,0.10873692088666873,0.08827903023095526,0.3166923725034843,0.10747821901215171,0.2623223387707427,0.2132171520025776,0.4241875840435831,0.07636635199368194,0.0634544416764679,0.2381687281562705,0
+Overall_Uncertainty,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.59599798281351,0.4973836190946347,0.8918410739701361,0.3172760636629066,0.2792196178051038,0.716639489471898,0.3308123858516533,0.2809034869135636,0.7187834492949164,0.3480164031919235,0.3047742004421416,0.7875754932795126,0.34032422236189885,0.6086383785857439,0.5226537972216503,0.892069035674793,0.2864135258457914,0.256243452133116,0.6644822620577554,0
+PPV,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.7857142857142857,1.0,0.0,0.9223669923995657,1.0,0.0,0.9013605442176871,1.0,0.0,0.9218181818181819,1.0,0.0,0.9127144298688193,0.797153024911032,1.0,0.0,0.9318048206937096,1.0,0.0,0
+Per_Sample_Accuracy,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.760406976744186,0.9811627906976744,0.09813953488372094,0.9136582809224318,0.9939207807118255,0.0713855421686747,0.8890645161290323,0.9941686893203884,0.07202830188679245,0.9106260869565218,0.9921537726838585,0.08189320388349515,0.9009855769230769,0.777456896551724,0.9765730337078652,0.12111111111111111,0.9258054272517321,0.9957824189526184,0.048906250000000005,0
+Positive-Rate,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,1.1382113821138211,1.0,2.3076923076923075,1.0696864111498259,1.0,6.217391304347826,1.0835380835380835,1.0,4.578947368421052,1.066925315227934,1.0,5.0588235294117645,1.0742547425474254,1.1330645161290323,1.0,2.375,1.0651221039448966,1.0,9.666666666666666,0
+Sample_Size,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,172.0,129.0,43.0,1908.0,1742.0,166.0,930.0,824.0,106.0,1150.0,1047.0,103.0,2080.0,348.0,267.0,81.0,1732.0,1604.0,128.0,0
+Selection-Rate,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.813953488372093,0.8527131782945736,0.6976744186046512,0.9654088050314465,0.9753157290470723,0.8614457831325302,0.9483870967741935,0.9648058252427184,0.8207547169811321,0.9565217391304348,0.9684813753581661,0.8349514563106796,0.9528846153846153,0.8074712643678161,0.8389513108614233,0.7037037037037037,0.9821016166281755,0.9881546134663342,0.90625,0
+Statistical_Bias,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.27685883580701437,0.15177998477621654,0.6520953888994078,0.12810491537979324,0.06947590409695573,0.7433563470346304,0.14717405086035498,0.07096334887945019,0.7396044134288979,0.13493220087245378,0.07844588681600942,0.7091183253102911,0.1404057203381981,0.2752245486786175,0.1630144388115851,0.6451023182403169,0.11331741071783666,0.06052480536803965,0.7748747465074804,0
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+PPV,101,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Law_School,200,OK,2023-08-07 01:00:06.671,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.0,0.8244274809160306,1.0,0.0,0.9243835616438356,1.0,0.0,0.9060710194730813,1.0,0.0,0.9270544783010157,1.0,0.0,0.9176891615541922,0.8244274809160306,1.0,0.0,0.9321133412042503,1.0,0.0,0
+Per_Sample_Accuracy,101,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Law_School,200,OK,2023-08-07 01:00:06.671,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.0,0.7649709302325582,0.9370149253731344,0.15828947368421054,0.9068684486373165,0.986328530259366,0.10997109826589595,0.8853172043010753,0.9824909090909092,0.1218095238095238,0.9030739130434783,0.9830316091954021,0.11556603773584906,0.8951346153846155,0.7650574712643678,0.9422407407407407,0.15173076923076925,0.921270207852194,0.9896404002501563,0.09928571428571428,0
+Positive-Rate,101,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Law_School,200,OK,2023-08-07 01:00:06.671,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.0,1.065040650406504,1.0,1.5333333333333334,1.0598141695702672,1.0,3.942857142857143,1.0724815724815724,1.0,3.5652173913043477,1.0504364694471386,1.0,2.925925925925926,1.0601626016260162,1.0564516129032258,1.0,1.4375,1.060738885410144,1.0,6.388888888888889,0
+Sample_Size,101,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Law_School,200,OK,2023-08-07 01:00:06.671,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.0,172.0,134.0,38.0,1908.0,1735.0,173.0,930.0,825.0,105.0,1150.0,1044.0,106.0,2080.0,348.0,270.0,78.0,1732.0,1599.0,133.0,0
+Selection-Rate,101,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Law_School,200,OK,2023-08-07 01:00:06.671,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.0,0.7616279069767442,0.8059701492537313,0.6052631578947368,0.9564989517819706,0.9723342939481268,0.7976878612716763,0.9387096774193548,0.9587878787878787,0.780952380952381,0.9417391304347826,0.9616858237547893,0.7452830188679245,0.9403846153846154,0.7528735632183908,0.8,0.5897435897435898,0.9780600461893765,0.9874921826141339,0.8646616541353384,0
+Statistical_Bias,101,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Law_School,200,OK,2023-08-07 01:00:06.671,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.0,0.2877847093023256,0.18303380597014923,0.6571694736842105,0.13291773060796644,0.07233455331412102,0.7405004624277457,0.15161317204301075,0.07806539393939393,0.7294885714285714,0.14096152173913043,0.08201439655172413,0.7215350943396226,0.14572403846153845,0.2892299712643678,0.18118155555555554,0.6632437179487178,0.1168902829099307,0.06323199499687303,0.7620000751879699,0
+Std,101,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Law_School,200,OK,2023-08-07 01:00:06.671,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.0,0.07305468283664128,0.06585784196897422,0.09843301642262514,0.03967207422680322,0.03487349564038114,0.08779654733340607,0.041894501288248155,0.03603838511536694,0.08790684264660058,0.04286768423875831,0.03792988317981428,0.09150036636647116,0.04243255916953982,0.07455480562429982,0.06745286046536986,0.09913846194367282,0.03597843574791368,0.031968851428551576,0.08418388933182348,0
+TNR,101,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Law_School,200,OK,2023-08-07 01:00:06.671,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.0,0.5306122448979592,1.0,0.0,0.25806451612903225,1.0,0.0,0.29310344827586204,1.0,0.0,0.33613445378151263,1.0,0.0,0.3148936170212766,0.54,1.0,0.0,0.14814814814814814,1.0,0.0,0
+TPR,101,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Law_School,200,OK,2023-08-07 01:00:06.671,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.0,0.8780487804878049,1.0,0.0,0.9796747967479674,1.0,0.0,0.9717444717444718,1.0,0.0,0.973811833171678,1.0,0.0,0.9728997289972899,0.8709677419354839,1.0,0.0,0.9887288666249218,1.0,0.0,0
diff --git a/docs/examples/pub_cov_subgroup_metrics.csv b/docs/examples/pub_cov_subgroup_metrics.csv
new file mode 100644
index 00000000..788e1430
--- /dev/null
+++ b/docs/examples/pub_cov_subgroup_metrics.csv
@@ -0,0 +1,153 @@
+Metric,Bootstrap_Model_Seed,Model_Name,Model_Params,Run_Number,Dataset_Name,Num_Estimators,Test_Set_Index,Tag,Record_Create_Date_Time,Session_Uuid,Experiment_Iteration,Dataset_Split_Seed,Model_Init_Seed,Fair_Intervention_Params_Lst,Intervention_Param,RAC1P_dis,RAC1P_dis_correct,RAC1P_dis_incorrect,RAC1P_priv,RAC1P_priv_correct,RAC1P_priv_incorrect,SEX&RAC1P_dis,SEX&RAC1P_dis_correct,SEX&RAC1P_dis_incorrect,SEX&RAC1P_priv,SEX&RAC1P_priv_correct,SEX&RAC1P_priv_incorrect,SEX_dis,SEX_dis_correct,SEX_dis_incorrect,SEX_priv,SEX_priv_correct,SEX_priv_incorrect,overall
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+F1,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 9, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 300}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:06:10.855,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.0],0.0,0.5275229357798165,1.0,0.0,0.5458089668615984,1.0,0.0,0.5344129554655871,1.0,0.0,0.5384615384615384,1.0,0.0,0.5258964143426295,1.0,0.0,0.5503355704697986,1.0,0.0,0.5374077976817703
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+Std,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:13:20.565,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.6],0.6,0.08719113374979333,0.08411282120286062,0.09425399104205477,0.08246286338476243,0.08000786245602967,0.08845689162634371,0.08669444508147052,0.08406093977471062,0.09384918958744656,0.08390375810210444,0.08103893198525286,0.09035824585934837,0.08422602181233907,0.0810713362549355,0.09223203053785067,0.08527266765729623,0.08305264420803606,0.09010953034143314,0.08468515045632696
+TNR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 30, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 2, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:06:10.960,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.0],0.0,0.8870967741935484,1.0,0.0,0.8603696098562629,1.0,0.0,0.9022556390977443,1.0,0.0,0.8610687022900764,1.0,0.0,0.8766355140186916,1.0,0.0,0.8678756476683938,1.0,0.0,0.8729641693811075
+TNR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:13:20.565,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.6],0.6,0.8663594470046083,1.0,0.0,0.8583162217659137,1.0,0.0,0.8796992481203008,1.0,0.0,0.8549618320610687,1.0,0.0,0.8598130841121495,1.0,0.0,0.8652849740932642,1.0,0.0,0.8621064060803475
+TPR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 30, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 2, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:06:10.960,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.0],0.0,0.3726937269372694,1.0,0.0,0.42857142857142855,1.0,0.0,0.4090909090909091,1.0,0.0,0.4,1.0,0.0,0.41368078175895767,1.0,0.0,0.3897058823529412,1.0,0.0,0.40241796200345425
+TPR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:13:20.565,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.6],0.6,0.42435424354243545,1.0,0.0,0.474025974025974,1.0,0.0,0.474025974025974,1.0,0.0,0.4423529411764706,1.0,0.0,0.46905537459283386,1.0,0.0,0.43014705882352944,1.0,0.0,0.45077720207253885
diff --git a/docs/examples/ricci_group_metrics.csv b/docs/examples/ricci_group_metrics.csv
new file mode 100644
index 00000000..31cfbb3f
--- /dev/null
+++ b/docs/examples/ricci_group_metrics.csv
@@ -0,0 +1,133 @@
+Metric,Race,Model_Name,Experiment_Iteration,Intervention_Param,Test_Set_Index
+Accuracy_Parity,-0.2299465240641711,LGBMClassifier,Exp_iter_1,0.0,0
+Disparate_Impact,2.2647058823529416,LGBMClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FNR,0.0,LGBMClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FPR,0.0,LGBMClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_TPR,0.0,LGBMClassifier,Exp_iter_1,0.0,0
+IQR_Parity,2.7755575615628914e-17,LGBMClassifier,Exp_iter_1,0.0,0
+Jitter_Parity,0.0,LGBMClassifier,Exp_iter_1,0.0,0
+Label_Stability_Ratio,1.0,LGBMClassifier,Exp_iter_1,0.0,0
+Statistical_Parity_Difference,3.0714285714285716,LGBMClassifier,Exp_iter_1,0.0,0
+Std_Parity,-1.3877787807814457e-17,LGBMClassifier,Exp_iter_1,0.0,0
+Std_Ratio,0.9999999999999998,LGBMClassifier,Exp_iter_1,0.0,0
+Accuracy_Parity,-0.2299465240641711,LGBMClassifier,Exp_iter_1,0.4,0
+Disparate_Impact,2.2647058823529416,LGBMClassifier,Exp_iter_1,0.4,0
+Equalized_Odds_FNR,0.0,LGBMClassifier,Exp_iter_1,0.4,0
+Equalized_Odds_FPR,0.0,LGBMClassifier,Exp_iter_1,0.4,0
+Equalized_Odds_TPR,0.0,LGBMClassifier,Exp_iter_1,0.4,0
+IQR_Parity,2.7755575615628914e-17,LGBMClassifier,Exp_iter_1,0.4,0
+Jitter_Parity,0.0,LGBMClassifier,Exp_iter_1,0.4,0
+Label_Stability_Ratio,1.0,LGBMClassifier,Exp_iter_1,0.4,0
+Statistical_Parity_Difference,3.0714285714285716,LGBMClassifier,Exp_iter_1,0.4,0
+Std_Parity,0.0,LGBMClassifier,Exp_iter_1,0.4,0
+Std_Ratio,1.0,LGBMClassifier,Exp_iter_1,0.4,0
+Accuracy_Parity,-0.2299465240641711,LGBMClassifier,Exp_iter_1,0.7,0
+Disparate_Impact,2.2647058823529416,LGBMClassifier,Exp_iter_1,0.7,0
+Equalized_Odds_FNR,0.0,LGBMClassifier,Exp_iter_1,0.7,0
+Equalized_Odds_FPR,0.0,LGBMClassifier,Exp_iter_1,0.7,0
+Equalized_Odds_TPR,0.0,LGBMClassifier,Exp_iter_1,0.7,0
+IQR_Parity,0.0,LGBMClassifier,Exp_iter_1,0.7,0
+Jitter_Parity,0.0,LGBMClassifier,Exp_iter_1,0.7,0
+Label_Stability_Ratio,1.0000000000000002,LGBMClassifier,Exp_iter_1,0.7,0
+Statistical_Parity_Difference,3.0714285714285716,LGBMClassifier,Exp_iter_1,0.7,0
+Std_Parity,-1.3877787807814457e-17,LGBMClassifier,Exp_iter_1,0.7,0
+Std_Ratio,0.9999999999999998,LGBMClassifier,Exp_iter_1,0.7,0
+Accuracy_Parity,0.3529411764705882,LogisticRegression,Exp_iter_1,0.0,0
+Disparate_Impact,0.5384615384615384,LogisticRegression,Exp_iter_1,0.0,0
+Equalized_Odds_FNR,0.0,LogisticRegression,Exp_iter_1,0.0,0
+Equalized_Odds_FPR,-0.6,LogisticRegression,Exp_iter_1,0.0,0
+Equalized_Odds_TPR,0.0,LogisticRegression,Exp_iter_1,0.0,0
+IQR_Parity,-0.004259721871489895,LogisticRegression,Exp_iter_1,0.0,0
+Jitter_Parity,-0.0640968210033926,LogisticRegression,Exp_iter_1,0.0,0
+Label_Stability_Ratio,1.0935446085768203,LogisticRegression,Exp_iter_1,0.0,0
+Statistical_Parity_Difference,-0.8571428571428572,LogisticRegression,Exp_iter_1,0.0,0
+Std_Parity,-0.0011073640847593519,LogisticRegression,Exp_iter_1,0.0,0
+Std_Ratio,0.9758617440249395,LogisticRegression,Exp_iter_1,0.0,0
+Accuracy_Parity,0.0267379679144385,LogisticRegression,Exp_iter_1,0.4,0
+Disparate_Impact,1.1666666666666665,LogisticRegression,Exp_iter_1,0.4,0
+Equalized_Odds_FNR,0.0,LogisticRegression,Exp_iter_1,0.4,0
+Equalized_Odds_FPR,-0.0888888888888889,LogisticRegression,Exp_iter_1,0.4,0
+Equalized_Odds_TPR,0.0,LogisticRegression,Exp_iter_1,0.4,0
+IQR_Parity,-0.02897288592163355,LogisticRegression,Exp_iter_1,0.4,0
+Jitter_Parity,-0.057898718189863724,LogisticRegression,Exp_iter_1,0.4,0
+Label_Stability_Ratio,1.1116523646686882,LogisticRegression,Exp_iter_1,0.4,0
+Statistical_Parity_Difference,0.2142857142857142,LogisticRegression,Exp_iter_1,0.4,0
+Std_Parity,-0.02372510499403515,LogisticRegression,Exp_iter_1,0.4,0
+Std_Ratio,0.6205686094932984,LogisticRegression,Exp_iter_1,0.4,0
+Accuracy_Parity,-0.06417112299465233,LogisticRegression,Exp_iter_1,0.7,0
+Disparate_Impact,1.5555555555555554,LogisticRegression,Exp_iter_1,0.7,0
+Equalized_Odds_FNR,0.0,LogisticRegression,Exp_iter_1,0.7,0
+Equalized_Odds_FPR,0.0222222222222222,LogisticRegression,Exp_iter_1,0.7,0
+Equalized_Odds_TPR,0.0,LogisticRegression,Exp_iter_1,0.7,0
+IQR_Parity,-0.026239708082403335,LogisticRegression,Exp_iter_1,0.7,0
+Jitter_Parity,-0.005885577620717217,LogisticRegression,Exp_iter_1,0.7,0
+Label_Stability_Ratio,0.9885615043717811,LogisticRegression,Exp_iter_1,0.7,0
+Statistical_Parity_Difference,0.7142857142857142,LogisticRegression,Exp_iter_1,0.7,0
+Std_Parity,-0.01916160626108921,LogisticRegression,Exp_iter_1,0.7,0
+Std_Ratio,0.749870952094999,LogisticRegression,Exp_iter_1,0.7,0
+Accuracy_Parity,-0.06417112299465233,MLPClassifier,Exp_iter_1,0.7,0
+Disparate_Impact,2.0,MLPClassifier,Exp_iter_1,0.7,0
+Equalized_Odds_FNR,-0.14285714285714285,MLPClassifier,Exp_iter_1,0.7,0
+Equalized_Odds_FPR,0.1222222222222222,MLPClassifier,Exp_iter_1,0.7,0
+Equalized_Odds_TPR,0.1428571428571429,MLPClassifier,Exp_iter_1,0.7,0
+IQR_Parity,-0.05854261516211538,MLPClassifier,Exp_iter_1,0.7,0
+Jitter_Parity,-0.006295515008201247,MLPClassifier,Exp_iter_1,0.7,0
+Label_Stability_Ratio,1.0171217205613179,MLPClassifier,Exp_iter_1,0.7,0
+Statistical_Parity_Difference,1.0,MLPClassifier,Exp_iter_1,0.7,0
+Std_Parity,0.004367751375921741,MLPClassifier,Exp_iter_1,0.7,0
+Std_Ratio,1.021641746544735,MLPClassifier,Exp_iter_1,0.7,0
+Accuracy_Parity,0.0,MLPClassifier,Exp_iter_1,0.0,0
+Disparate_Impact,1.0,MLPClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FNR,0.0,MLPClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FPR,0.0,MLPClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_TPR,0.0,MLPClassifier,Exp_iter_1,0.0,0
+IQR_Parity,-0.14246344136147643,MLPClassifier,Exp_iter_1,0.0,0
+Jitter_Parity,-0.11034584688143848,MLPClassifier,Exp_iter_1,0.0,0
+Label_Stability_Ratio,1.1957288401253918,MLPClassifier,Exp_iter_1,0.0,0
+Statistical_Parity_Difference,0.0,MLPClassifier,Exp_iter_1,0.0,0
+Std_Parity,-0.12317270176886641,MLPClassifier,Exp_iter_1,0.0,0
+Std_Ratio,0.11675594996318969,MLPClassifier,Exp_iter_1,0.0,0
+Accuracy_Parity,0.07219251336898402,MLPClassifier,Exp_iter_1,0.4,0
+Disparate_Impact,1.25,MLPClassifier,Exp_iter_1,0.4,0
+Equalized_Odds_FNR,-0.14285714285714285,MLPClassifier,Exp_iter_1,0.4,0
+Equalized_Odds_FPR,-0.04444444444444445,MLPClassifier,Exp_iter_1,0.4,0
+Equalized_Odds_TPR,0.1428571428571429,MLPClassifier,Exp_iter_1,0.4,0
+IQR_Parity,-0.1782305060524368,MLPClassifier,Exp_iter_1,0.4,0
+Jitter_Parity,-0.09088436836585866,MLPClassifier,Exp_iter_1,0.4,0
+Label_Stability_Ratio,1.1823216340621405,MLPClassifier,Exp_iter_1,0.4,0
+Statistical_Parity_Difference,0.25,MLPClassifier,Exp_iter_1,0.4,0
+Std_Parity,-0.08577780370352184,MLPClassifier,Exp_iter_1,0.4,0
+Std_Ratio,0.6190997914544973,MLPClassifier,Exp_iter_1,0.4,0
+Accuracy_Parity,0.11764705882352944,RandomForestClassifier,Exp_iter_1,0.0,0
+Disparate_Impact,0.7777777777777777,RandomForestClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FNR,0.0,RandomForestClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_FPR,-0.2,RandomForestClassifier,Exp_iter_1,0.0,0
+Equalized_Odds_TPR,0.0,RandomForestClassifier,Exp_iter_1,0.0,0
+IQR_Parity,-0.07018048128342244,RandomForestClassifier,Exp_iter_1,0.0,0
+Jitter_Parity,-0.08421653723159321,RandomForestClassifier,Exp_iter_1,0.0,0
+Label_Stability_Ratio,1.1554112554112557,RandomForestClassifier,Exp_iter_1,0.0,0
+Statistical_Parity_Difference,-0.2857142857142858,RandomForestClassifier,Exp_iter_1,0.0,0
+Std_Parity,-0.04209583589618668,RandomForestClassifier,Exp_iter_1,0.0,0
+Std_Ratio,0.4548023847532704,RandomForestClassifier,Exp_iter_1,0.0,0
+Accuracy_Parity,0.14438502673796794,RandomForestClassifier,Exp_iter_1,0.4,0
+Disparate_Impact,0.9545454545454546,RandomForestClassifier,Exp_iter_1,0.4,0
+Equalized_Odds_FNR,0.0,RandomForestClassifier,Exp_iter_1,0.4,0
+Equalized_Odds_FPR,-0.2888888888888889,RandomForestClassifier,Exp_iter_1,0.4,0
+Equalized_Odds_TPR,0.0,RandomForestClassifier,Exp_iter_1,0.4,0
+IQR_Parity,-0.07059491978609625,RandomForestClassifier,Exp_iter_1,0.4,0
+Jitter_Parity,-0.10266600381584051,RandomForestClassifier,Exp_iter_1,0.4,0
+Label_Stability_Ratio,1.229393468118196,RandomForestClassifier,Exp_iter_1,0.4,0
+Statistical_Parity_Difference,-0.0714285714285714,RandomForestClassifier,Exp_iter_1,0.4,0
+Std_Parity,-0.04122312272769589,RandomForestClassifier,Exp_iter_1,0.4,0
+Std_Ratio,0.607473282709729,RandomForestClassifier,Exp_iter_1,0.4,0
+Accuracy_Parity,-0.06417112299465233,RandomForestClassifier,Exp_iter_1,0.7,0
+Disparate_Impact,1.5555555555555554,RandomForestClassifier,Exp_iter_1,0.7,0
+Equalized_Odds_FNR,0.0,RandomForestClassifier,Exp_iter_1,0.7,0
+Equalized_Odds_FPR,0.0222222222222222,RandomForestClassifier,Exp_iter_1,0.7,0
+Equalized_Odds_TPR,0.0,RandomForestClassifier,Exp_iter_1,0.7,0
+IQR_Parity,-0.03575165472525095,RandomForestClassifier,Exp_iter_1,0.7,0
+Jitter_Parity,-0.04552387606479004,RandomForestClassifier,Exp_iter_1,0.7,0
+Label_Stability_Ratio,1.071062271062271,RandomForestClassifier,Exp_iter_1,0.7,0
+Statistical_Parity_Difference,0.7142857142857142,RandomForestClassifier,Exp_iter_1,0.7,0
+Std_Parity,-0.016581787315341434,RandomForestClassifier,Exp_iter_1,0.7,0
+Std_Ratio,0.8265703729361945,RandomForestClassifier,Exp_iter_1,0.7,0
diff --git a/docs/examples/ricci_subgroup_metrics.csv b/docs/examples/ricci_subgroup_metrics.csv
new file mode 100644
index 00000000..42f9fdfd
--- /dev/null
+++ b/docs/examples/ricci_subgroup_metrics.csv
@@ -0,0 +1,229 @@
+Metric,Bootstrap_Model_Seed,Model_Name,Model_Params,Run_Number,Dataset_Name,Num_Estimators,Tag,Record_Create_Date_Time,Session_Uuid,Experiment_Iteration,Dataset_Split_Seed,Model_Init_Seed,Fair_Intervention_Params_Lst,Intervention_Param,Race_dis,Race_priv,overall,Test_Set_Index
+Accuracy,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.322,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,0.18181818181818182,0.4117647058823529,0.28205128205128205,0
+Aleatoric_Uncertainty,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.322,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,0.9735094905965549,0.9735094905965547,0.9735094905965548,0
+F1,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.322,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,0.3076923076923077,0.5833333333333334,0.44,0
+FNR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.322,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,0.0,0.0,0.0,0
+FPR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.322,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,1.0,1.0,1.0,0
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+TPR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.322,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,1.0,1.0,1.0,0
+Accuracy,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.429,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.18181818181818182,0.4117647058823529,0.28205128205128205,0
+Aleatoric_Uncertainty,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.429,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.9729961395248143,0.9729961395248147,0.972996139524815,0
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+FPR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.429,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,1.0,1.0,1.0,0
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+Std,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.429,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.061795142880967324,0.061795142880967324,0.061795142880967366,0
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+TPR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.429,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,1.0,1.0,1.0,0
+Accuracy,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.18181818181818182,0.4117647058823529,0.28205128205128205,0
+Aleatoric_Uncertainty,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.9778290634912143,0.9778290634912146,0.9778290634912145,0
+F1,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.3076923076923077,0.5833333333333334,0.44,0
+FNR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.0,0.0,0.0,0
+FPR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,1.0,1.0,1.0,0
+IQR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.0793650793650793,0.0793650793650793,0.07936507936507926,0
+Jitter,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.24201005025125627,0.24201005025125627,0.24201005025125627,0
+Label_Stability,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.7200000000000002,0.72,0.7199999999999998,0
+Mean,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.43650793650793657,0.43650793650793657,0.43650793650793657,0
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+PPV,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.18181818181818182,0.4117647058823529,0.28205128205128205,0
+Per_Sample_Accuracy,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.2709090909090909,0.43647058823529417,0.34307692307692317,0
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+Sample_Size,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,,,39.0,0
+Selection-Rate,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.954,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,1.0,1.0,1.0,0
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+Positive-Rate,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.986,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,2.0,1.0,1.3636363636363635,0
+Sample_Size,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.986,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,,,39.0,0
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+Std,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.986,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.20618840235218705,0.2018206509762653,0.20428451072678522,0
+TNR,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.986,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.7777777777777778,0.9,0.8214285714285714,0
+TPR,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.986,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,1.0,0.8571428571428571,0.9090909090909091,0
+Accuracy,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,1.0,1.0,1.0,0
+Aleatoric_Uncertainty,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,0.017886152509533817,0.08715135409347269,0.048078676276891795,0
+F1,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,1.0,1.0,1.0,0
+FNR,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,0.0,0.0,0.0,0
+FPR,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,0.0,0.0,0.0,0
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+Mean,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,0.8296441174696434,0.4997150126395015,0.6858288666462482,0
+Overall_Uncertainty,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,0.03891369064632084,0.2909148674422839,0.14876035745481758,0
+PPV,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,1.0,1.0,1.0,0
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+Positive-Rate,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,1.0,1.0,1.0,0
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+TPR,101,MLPClassifier,"{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:50:29.351,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.0],0.0,1.0,1.0,1.0,0
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+Aleatoric_Uncertainty,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.019742903314295127,0.049345639638436564,0.03264666017353627,0
+F1,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.8888888888888888,0.8571428571428571,0.8695652173913043,0
+FNR,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.0,0.14285714285714285,0.09090909090909091,0
+FPR,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.05555555555555555,0.1,0.07142857142857142,0
+IQR,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.06900940802527812,0.24723991407771492,0.14669962861223776,0
+Jitter,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.08922795797166976,0.1801123263375284,0.1288442211055247,0
+Label_Stability,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.879090909090909,0.7435294117647058,0.8199999999999998,0
+Mean,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.7366099368767967,0.5436120808232203,0.6524826662893403,0
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+PPV,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.8,0.8571428571428571,0.8333333333333334,0
+Per_Sample_Accuracy,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.9018181818181819,0.8264705882352942,0.8689743589743592,0
+Positive-Rate,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,1.25,1.0,1.0909090909090908,0
+Sample_Size,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,,,39.0,0
+Selection-Rate,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.22727272727272727,0.4117647058823529,0.3076923076923077,0
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+Std,101,MLPClassifier,"{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.460,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.13941977240459089,0.22519757610811272,0.17681009709586964,0
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+PPV,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.450,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.6666666666666666,0.6363636363636364,0.6470588235294118,0
+Per_Sample_Accuracy,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.450,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.9136363636363637,0.7999999999999999,0.864102564102564,0
+Positive-Rate,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.450,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,1.5,1.5714285714285714,1.5454545454545454,0
+Sample_Size,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.450,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,,,39.0,0
+Selection-Rate,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.450,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.2727272727272727,0.6470588235294118,0.4358974358974359,0
+Statistical_Bias,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.450,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.12228409090909098,0.22866470588235302,0.1686551282051283,0
+Std,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.450,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.06379679289045975,0.10501991561815564,0.08176584638714769,0
+TNR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.450,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,0.8888888888888888,0.6,0.7857142857142857,0
+TPR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:52:13.450,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.4],0.4,1.0,1.0,1.0,0
+Accuracy,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.8181818181818182,0.8823529411764706,0.8461538461538461,0
+Aleatoric_Uncertainty,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.3736138589403957,0.49733889404460735,0.4275452844986417,0
+F1,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.6666666666666666,0.875,0.7857142857142857,0
+FNR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.0,0.0,0.0,0
+FPR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.2222222222222222,0.2,0.21428571428571427,0
+IQR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.10521006329529055,0.1409617180205415,0.12079411791911789,0
+Jitter,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.08761534947463821,0.13313922553942825,0.10745909032341451,0
+Label_Stability,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.8600000000000001,0.8029411764705883,0.8351282051282052,0
+Mean,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.6203538533057853,0.5106819312876667,0.5725481437081439,0
+Overall_Uncertainty,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.42535702360304817,0.5548910385593142,0.4818205685839844,0
+PPV,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.5,0.7777777777777778,0.6470588235294118,0
+Per_Sample_Accuracy,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.7995454545454543,0.8585294117647058,0.8252564102564103,0
+Positive-Rate,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,2.0,1.2857142857142858,1.5454545454545454,0
+Sample_Size,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,,,39.0,0
+Selection-Rate,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.36363636363636365,0.5294117647058824,0.4358974358974359,0
+Statistical_Bias,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.21751490702479329,0.21198756429844656,0.21510555250305238,0
+Std,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.07902925444305965,0.09561104175840109,0.08625721301641362,0
+TNR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,0.7777777777777778,0.8,0.7857142857142857,0
+TPR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 5, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Ricci,200,OK,2023-08-06 20:53:03.975,d6a4d686-4369-4bca-95c8-7be5d0740b15,Exp_iter_1,100,100,[0.7],0.7,1.0,1.0,1.0,0
diff --git a/requirements.txt b/requirements.txt
index 0b5c8ab7..f9da8a5d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,6 +16,5 @@ river==0.15.0
 python-dotenv~=1.0.0
 pytest~=7.2.2
 pymongo==4.3.3
-datapane~=0.16.0
 requests-toolbelt==1.0.0
 colorama~=0.4.6
\ No newline at end of file
diff --git a/tests/utils/test_data_viz_utils.py b/tests/utils/test_data_viz_utils.py
new file mode 100644
index 00000000..6595c281
--- /dev/null
+++ b/tests/utils/test_data_viz_utils.py
@@ -0,0 +1,43 @@
+import pandas as pd
+
+from virny.utils.data_viz_utils import rank_with_tolerance
+
+
+def test_rank_with_tolerance_true1():
+    tolerance = 0.005
+    pd_series = pd.Series([0.025, 0.027, 0.001, 0.002]) # should be only positive numbers
+    expected_ranks = [2, 2, 1, 1]
+    actual_ranks = rank_with_tolerance(pd_series, tolerance)
+    assert actual_ranks.tolist() == expected_ranks
+
+
+def test_rank_with_tolerance_true2():
+    tolerance = 0.005
+    pd_series = pd.Series([0.429, 0.289, 0.377, 0.259]) # should be only positive numbers
+    expected_ranks = [4, 2, 3, 1]
+    actual_ranks = rank_with_tolerance(pd_series, tolerance)
+    assert actual_ranks.tolist() == expected_ranks
+
+
+def test_rank_with_tolerance_true3():
+    tolerance = 0.005
+    pd_series = pd.Series([0.313, 0.157, 0.274, 0.147]) # should be only positive numbers
+    expected_ranks = [4, 2, 3, 1]
+    actual_ranks = rank_with_tolerance(pd_series, tolerance)
+    assert actual_ranks.tolist() == expected_ranks
+
+
+def test_rank_with_tolerance_true4():
+    tolerance = 0.005
+    pd_series = pd.Series([0.001, 0.001, 0.0, 0.0]) # should be only positive numbers
+    expected_ranks = [1, 1, 1, 1]
+    actual_ranks = rank_with_tolerance(pd_series, tolerance)
+    assert actual_ranks.tolist() == expected_ranks
+
+
+def test_rank_with_tolerance_true5():
+    tolerance = 0.01
+    pd_series = pd.Series([0.099, 0.092, 0.075, 0.085]) # should be only positive numbers
+    expected_ranks = [2, 2, 1, 1]
+    actual_ranks = rank_with_tolerance(pd_series, tolerance)
+    assert actual_ranks.tolist() == expected_ranks
diff --git a/virny/configs/constants.py b/virny/configs/constants.py
index 81d145b2..fff71dab 100644
--- a/virny/configs/constants.py
+++ b/virny/configs/constants.py
@@ -54,6 +54,7 @@ class ReportType(Enum):
 
 # Stability disparity metrics
 LABEL_STABILITY_RATIO = 'Label_Stability_Ratio'
+LABEL_STABILITY_DIFFERENCE = 'Label_Stability_Difference'
 IQR_PARITY = 'IQR_Parity'
 STD_PARITY = 'Std_Parity'
 STD_RATIO = 'Std_Ratio'
diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py
index cbd58c4c..1aae0c97 100644
--- a/virny/custom_classes/metrics_composer.py
+++ b/virny/custom_classes/metrics_composer.py
@@ -32,7 +32,8 @@ def __init__(self, models_metrics_dct: dict, sensitive_attributes_dct: dict):
             POSITIVE_RATE: [(STATISTICAL_PARITY_DIFFERENCE, self._difference_operation),
                             (DISPARATE_IMPACT, self._ratio_operation)],
             # Stability disparity metrics
-            LABEL_STABILITY: [(LABEL_STABILITY_RATIO, self._ratio_operation)],
+            LABEL_STABILITY: [(LABEL_STABILITY_RATIO, self._ratio_operation),
+                              (LABEL_STABILITY_DIFFERENCE, self._difference_operation)],
             JITTER: [(JITTER_PARITY, self._difference_operation)],
             IQR: [(IQR_PARITY, self._difference_operation)],
             STD: [(STD_PARITY, self._difference_operation),
diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py
new file mode 100644
index 00000000..18fee7da
--- /dev/null
+++ b/virny/custom_classes/metrics_interactive_visualizer.py
@@ -0,0 +1,828 @@
+import pandas as pd
+import gradio as gr
+import altair as alt
+from pprint import pprint
+
+from virny.configs.constants import *
+from virny.utils.common_helpers import str_to_float
+from virny.utils.protected_groups_partitioning import create_test_protected_groups
+from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank,
+                                        create_subgroup_sorted_matrix_by_rank, create_flexible_bar_plot_for_model_selection,
+                                        compute_proportions, compute_base_rates, create_col_facet_bar_chart,
+                                        create_model_performance_summary_visualization)
+
+
+class MetricsInteractiveVisualizer:
+    """
+    Class to create useful visualizations of models metrics.
+
+    Parameters
+    ----------
+    X_data
+        An original features dataframe
+    y_data
+        An original target column pandas series
+    model_metrics_dct
+        Dictionary where keys are model names and values are dataframes of subgroup metrics for each model
+    model_composed_metrics_df
+        Dataframe of all model composed metrics
+    sensitive_attributes_dct
+        A dictionary where keys are sensitive attributes names (including attributes intersections),
+         and values are privilege values for these attributes
+
+    """
+    def __init__(self, X_data: pd.DataFrame, y_data: pd.DataFrame, model_metrics_dct: dict,
+                 model_composed_metrics_df: pd.DataFrame, sensitive_attributes_dct: dict):
+        self.X_data = X_data
+        self.y_data = y_data
+        self.model_names = list(model_metrics_dct.keys())
+        self.sensitive_attributes_dct = sensitive_attributes_dct
+        self.group_names = list(self.sensitive_attributes_dct.keys())
+
+        # Technical attributes
+        self.demo = None
+        self.max_groups = 8
+
+        # Metric names
+        self.all_accuracy_metrics = [STATISTICAL_BIAS, TPR, TNR, PPV, FNR, FPR, F1, ACCURACY, POSITIVE_RATE]
+        self.all_stability_metrics = [STD, IQR, JITTER, LABEL_STABILITY]
+        self.all_uncertainty_metrics = [ALEATORIC_UNCERTAINTY, OVERALL_UNCERTAINTY]
+        self.all_error_disparity_metrics = [EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY]
+        self.all_stability_disparity_metrics = [LABEL_STABILITY_RATIO, LABEL_STABILITY_DIFFERENCE, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY]
+        self.all_uncertainty_disparity_metrics = [OVERALL_UNCERTAINTY_PARITY, OVERALL_UNCERTAINTY_RATIO, ALEATORIC_UNCERTAINTY_PARITY, ALEATORIC_UNCERTAINTY_RATIO]
+
+        self.all_overall_metrics = self.all_accuracy_metrics + self.all_stability_metrics + self.all_uncertainty_metrics
+        self.all_disparity_metrics = self.all_error_disparity_metrics + self.all_stability_disparity_metrics + self.all_uncertainty_disparity_metrics
+
+        # Create one metrics df with all model_dfs
+        models_metrics_df = pd.DataFrame()
+        for model_name in model_metrics_dct.keys():
+            model_metrics_df = model_metrics_dct[model_name]
+            models_metrics_df = pd.concat([models_metrics_df, model_metrics_df])
+
+        models_metrics_df = models_metrics_df.reset_index(drop=True)
+
+        self.models_metrics_dct = model_metrics_dct
+        self.models_metrics_df = self._align_input_metric_df(models_metrics_df, allowed_cols=["Metric", "Model_Name", "overall"],
+                                                             sensitive_attrs=list(self.sensitive_attributes_dct.keys()))
+        self.model_composed_metrics_df =  self._align_input_metric_df(model_composed_metrics_df, allowed_cols=["Metric", "Model_Name"],
+                                                                      sensitive_attrs=list(self.sensitive_attributes_dct.keys()))
+
+        self.melted_model_metrics_df = self.models_metrics_df.melt(id_vars=["Metric", "Model_Name"],
+                                                                   var_name="Subgroup",
+                                                                   value_name="Value")
+        self.sorted_model_metrics_df = self.melted_model_metrics_df.sort_values(by=['Value'])
+        self.melted_model_composed_metrics_df = self.model_composed_metrics_df.melt(id_vars=["Metric", "Model_Name"],
+                                                                                    var_name="Subgroup",
+                                                                                    value_name="Value")
+        self.sorted_model_composed_metrics_df = self.melted_model_composed_metrics_df.sort_values(by=['Value'])
+
+    def _align_input_metric_df(self, model_metrics_df: pd.DataFrame, allowed_cols: list, sensitive_attrs: list):
+        # Filter columns in the input dataframe based on allowed_cols and sensitive_attrs
+        filtered_cols = allowed_cols
+        for col in model_metrics_df.columns:
+            for sensitive_attr in sensitive_attrs:
+                if sensitive_attr in col:
+                    filtered_cols.append(col)
+                    break
+
+        return model_metrics_df[filtered_cols]
+
+    def __variable_inputs(self, k):
+        k = int(k)
+        return [gr.Textbox(visible=True)] * k + [gr.Textbox(value='', visible=False)] * (self.max_groups - k)
+
+    def start_web_app(self):
+        with gr.Blocks(theme=gr.themes.Soft()) as demo:
+            # ==================================== Dataset Statistics ====================================
+            gr.Markdown(
+                """
+                ## Dataset Statistics
+                """)
+            with gr.Row():
+                with gr.Column(scale=2):
+                    default_val = 5
+                    s = gr.Slider(1, self.max_groups, value=default_val, step=1, label="How many groups to show:")
+                    grp_names = []
+                    grp_dis_values = []
+                    sensitive_attr_items = list(self.sensitive_attributes_dct.items())
+                    for i in range(self.max_groups):
+                        visibility = True if i + 1 <= default_val else False
+                        with gr.Row():
+                            if visibility and i + 1 <= len(sensitive_attr_items):
+                                grp, dis_value = sensitive_attr_items[i]
+                                if dis_value is None:
+                                    dis_value = '-'
+                                elif isinstance(dis_value, str):
+                                    dis_value = f"'{dis_value}'"
+                                grp_name = gr.Text(label=f"Group {i + 1}", value=grp, interactive=True, visible=visibility)
+                                grp_dis_value = gr.Text(label="Disadvantage value", value=dis_value, interactive=True, visible=visibility)
+                            else:
+                                grp_name = gr.Text(label=f"Group {i + 1}", interactive=True, visible=visibility)
+                                grp_dis_value = gr.Text(label="Disadvantage value", interactive=True, visible=visibility)
+                        grp_names.append(grp_name)
+                        grp_dis_values.append(grp_dis_value)
+
+                    s.change(self.__variable_inputs, s, grp_names)
+                    s.change(self.__variable_inputs, s, grp_dis_values)
+                    btn_view0 = gr.Button("Submit")
+                with gr.Column(scale=4):
+                    dataset_proportions_bar_chart = gr.Plot(label="Group Proportions and Base Rates")
+
+            btn_view0.click(self._create_dataset_proportions_bar_chart,
+                            inputs=[grp_names[0], grp_names[1], grp_names[2], grp_names[3], grp_names[4], grp_names[5], grp_names[6], grp_names[7],
+                                    grp_dis_values[0], grp_dis_values[1], grp_dis_values[2], grp_dis_values[3], grp_dis_values[4], grp_dis_values[5], grp_dis_values[6], grp_dis_values[7]],
+                            outputs=[dataset_proportions_bar_chart])
+
+            # ==================================== Bar Chart for Model Selection ====================================
+            gr.Markdown(
+                """
+                ## Bar Chart for Model Selection
+                Select input arguments to create a bar chart for model selection. Default values display the lowest and greatest limits of constraints.
+                """)
+            with gr.Row():
+                with gr.Column(scale=2):
+                    group_name = gr.Dropdown(
+                        self.group_names,
+                        value=self.group_names[0], multiselect=False, label="Group Name for Disparity Metrics",
+                    )
+                    with gr.Row():
+                        overall_metric1 = gr.Dropdown(
+                            sorted(self.all_overall_metrics),
+                            value='Accuracy', multiselect=False, label="Overall Constraint (C1)",
+                            scale=2
+                        )
+                        overall_metric_min_val1 = gr.Text(value="0.0", label="Min value", scale=1)
+                        overall_metric_max_val1 = gr.Text(value="1.0", label="Max value", scale=1)
+                    with gr.Row():
+                        disparity_metric1 = gr.Dropdown(
+                            sorted(self.all_disparity_metrics),
+                            value='Equalized_Odds_FPR', multiselect=False, label="Disparity Constraint (C2)",
+                            scale=2
+                        )
+                        disparity_metric_min_val1 = gr.Text(value="-1.0", label="Min value", scale=1)
+                        disparity_metric_max_val1 = gr.Text(value="1.0", label="Max value", scale=1)
+                    with gr.Row():
+                        overall_metric2 = gr.Dropdown(
+                            sorted(self.all_overall_metrics),
+                            value='Label_Stability', multiselect=False, label="Overall Constraint (C3)",
+                            scale=2
+                        )
+                        overall_metric_min_val2 = gr.Text(value="0.0", label="Min value", scale=1)
+                        overall_metric_max_val2 = gr.Text(value="1.0", label="Max value", scale=1)
+                    with gr.Row():
+                        disparity_metric2 = gr.Dropdown(
+                            sorted(self.all_disparity_metrics),
+                            value='Label_Stability_Ratio', multiselect=False, label="Disparity Constraint (C4)",
+                            scale=2
+                        )
+                        disparity_metric_min_val2 = gr.Text(value="0.7", label="Min value", scale=1)
+                        disparity_metric_max_val2 = gr.Text(value="1.5", label="Max value", scale=1)
+
+                    btn_view1 = gr.Button("Submit")
+                with gr.Column(scale=3):
+                    bar_plot_for_model_selection = gr.Plot(label="Bar Chart")
+                    df_with_models_satisfied_all_constraints = gr.DataFrame(label='Models that satisfy all 4 constraints')
+
+            btn_view1.click(self._create_bar_plot_for_model_selection,
+                            inputs=[group_name,
+                                    overall_metric1, overall_metric_min_val1, overall_metric_max_val1,
+                                    disparity_metric1, disparity_metric_min_val1, disparity_metric_max_val1,
+                                    overall_metric2, overall_metric_min_val2, overall_metric_max_val2,
+                                    disparity_metric2, disparity_metric_min_val2, disparity_metric_max_val2],
+                            outputs=[bar_plot_for_model_selection, df_with_models_satisfied_all_constraints])
+
+            # ======================================= Overall Metrics Heatmap =======================================
+            gr.Markdown(
+                """
+                ## Overall Metrics Heatmap
+                Select input arguments to create an overall metrics heatmap.
+                """)
+            with gr.Row():
+                with gr.Column(scale=1):
+                    model_names = gr.Dropdown(
+                        sorted(self.model_names), value=sorted(self.model_names)[:4], max_choices=5, multiselect=True,
+                        label="Model Names", info="Select model names to display on the heatmap:",
+                    )
+                    subgroup_tolerance = gr.Text(value="0.005", label="Tolerance", info="Define an acceptable tolerance for metric dense ranking.")
+                    accuracy_metrics = gr.Dropdown(
+                        sorted(self.all_accuracy_metrics),
+                        value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:",
+                    )
+                    uncertainty_metrics = gr.Dropdown(
+                        sorted(self.all_uncertainty_metrics),
+                        value=['Aleatoric_Uncertainty', 'Overall_Uncertainty'], multiselect=True, label="Uncertainty Metrics", info="Select uncertainty metrics to display on the heatmap:",
+                    )
+                    subgroup_stability_metrics = gr.Dropdown(
+                        sorted(self.all_stability_metrics),
+                        value=['Std', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:",
+                    )
+                    subgroup_btn_view2 = gr.Button("Submit")
+                with gr.Column(scale=2):
+                    subgroup_model_ranking_heatmap = gr.Plot(label="Heatmap")
+
+            subgroup_btn_view2.click(self._create_subgroup_model_rank_heatmap,
+                                     inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics, subgroup_tolerance],
+                                     outputs=[subgroup_model_ranking_heatmap])
+
+            # ======================================== Disparity Metrics Heatmap ========================================
+            gr.Markdown(
+                """
+                ## Disparity Metrics Heatmap
+                Select input arguments to create a disparity metrics heatmap.
+                """)
+            with gr.Row():
+                with gr.Column(scale=1):
+                    model_names = gr.Dropdown(
+                        sorted(self.model_names), value=sorted(self.model_names)[:4], max_choices=5, multiselect=True,
+                        label="Model Names", info="Select model names to display on the heatmap:",
+                    )
+                    group_tolerance = gr.Text(value="0.005", label="Tolerance", info="Define an acceptable tolerance for metric dense ranking.")
+                    fairness_metrics_vw2 = gr.Dropdown(
+                        sorted(self.all_error_disparity_metrics),
+                        value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Disparity Metrics", info="Select error disparity metrics to display on the heatmap:",
+                    )
+                    group_uncertainty_metrics_vw2 = gr.Dropdown(
+                        sorted(self.all_uncertainty_disparity_metrics),
+                        value=['Overall_Uncertainty_Parity'], multiselect=True, label="Uncertainty Disparity Metrics", info="Select uncertainty disparity metrics to display on the heatmap:",
+                    )
+                    group_stability_metrics_vw2 = gr.Dropdown(
+                        sorted(self.all_stability_disparity_metrics),
+                        value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Disparity Metrics", info="Select stability disparity metrics to display on the heatmap:",
+                    )
+                    group_btn_view2 = gr.Button("Submit")
+                with gr.Column(scale=2):
+                    group_model_ranking_heatmap = gr.Plot(label="Heatmap")
+
+            group_btn_view2.click(self._create_group_model_rank_heatmap,
+                                  inputs=[model_names, fairness_metrics_vw2, group_uncertainty_metrics_vw2, group_stability_metrics_vw2, group_tolerance],
+                                  outputs=[group_model_ranking_heatmap])
+
+            # ============================ Group Specific and Disparity Metrics Bar Charts ============================
+            with gr.Row():
+                # Scale column 1 to a half of a screen
+                with gr.Column():
+                    gr.Markdown(
+                        """
+                        ## Group Specific and Disparity Metrics Bar Charts
+                        """)
+                    model_name_vw3 = gr.Dropdown(
+                        sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, scale=1,
+                        label="Model Name", info="Select one model to display on the bar charts:",
+                    )
+                with gr.Column():
+                    pass
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown(
+                        """
+                        ### Group Specific Metrics
+                        """)
+                    accuracy_metrics = gr.Dropdown(
+                        sorted(self.all_accuracy_metrics),
+                        value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:",
+                    )
+                    uncertainty_metrics = gr.Dropdown(
+                        sorted(self.all_uncertainty_metrics),
+                        value=['Aleatoric_Uncertainty', 'Overall_Uncertainty'], multiselect=True, label="Uncertainty Metrics", info="Select uncertainty metrics to display on the heatmap:",
+                    )
+                    subgroup_stability_metrics = gr.Dropdown(
+                        sorted(self.all_stability_metrics),
+                        value=['Std', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:",
+                    )
+                    btn_view3 = gr.Button("Submit")
+                with gr.Column():
+                    gr.Markdown(
+                        """
+                        ### Disparity Metrics
+                        """)
+                    fairness_metrics_vw3 = gr.Dropdown(
+                        sorted(self.all_error_disparity_metrics),
+                        value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Disparity Metrics", info="Select error disparity metrics to display on the heatmap:",
+                    )
+                    group_uncertainty_metrics_vw3 = gr.Dropdown(
+                        sorted(self.all_uncertainty_disparity_metrics),
+                        value=['Aleatoric_Uncertainty_Ratio', 'Overall_Uncertainty_Parity'], multiselect=True, label="Uncertainty Disparity Metrics", info="Select uncertainty disparity metrics to display on the heatmap:",
+                    )
+                    group_stability_metrics_vw3 = gr.Dropdown(
+                        sorted(self.all_stability_disparity_metrics),
+                        value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Disparity Metrics", info="Select stability disparity metrics to display on the heatmap:",
+                    )
+            with gr.Row():
+                with gr.Column():
+                    subgroup_metrics_bar_chart = gr.Plot(label="Group Specific Bar Chart")
+                with gr.Column():
+                    group_metrics_bar_chart = gr.Plot(label="Disparity Bar Chart")
+
+            btn_view3.click(self._create_subgroup_metrics_bar_chart_per_one_model,
+                            inputs=[model_name_vw3, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics],
+                            outputs=[subgroup_metrics_bar_chart])
+            btn_view3.click(self._create_group_metrics_bar_chart_per_one_model,
+                            inputs=[model_name_vw3, fairness_metrics_vw3, group_uncertainty_metrics_vw3, group_stability_metrics_vw3],
+                            outputs=[group_metrics_bar_chart])
+
+            # ============================ Model Performance Summary ============================
+            with gr.Row():
+                # Scale column 1 to a half of a screen
+                with gr.Column():
+                    gr.Markdown(
+                        """
+                        ## Model Performance Summary
+                        """)
+                    model_name_vw4 = gr.Dropdown(
+                        sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, scale=1,
+                        label="Model Name", info="Select one model to generate a performance summary:",
+                    )
+                with gr.Column():
+                    pass
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown(
+                        """
+                        ### Overall Metric Constraints
+                        """)
+                    with gr.Row():
+                        accuracy_metric_vw4 = gr.Dropdown(
+                            sorted([metric for metric in self.all_accuracy_metrics if metric != POSITIVE_RATE]),
+                            value=ACCURACY, multiselect=False, label="Accuracy Metric",
+                            scale=2
+                        )
+                        accuracy_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1)
+                        accuracy_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1)
+                    with gr.Row():
+                        subgroup_stability_metric_vw4 = gr.Dropdown(
+                            sorted(self.all_stability_metrics),
+                            value=LABEL_STABILITY, multiselect=False, label="Stability Metric",
+                            scale=2
+                        )
+                        subgroup_stab_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1)
+                        subgroup_stab_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1)
+                    with gr.Row():
+                        subgroup_uncertainty_metric_vw4 = gr.Dropdown(
+                            sorted(self.all_uncertainty_metrics),
+                            value=ALEATORIC_UNCERTAINTY, multiselect=False, label="Uncertainty Metric",
+                            scale=2
+                        )
+                        subgroup_uncertainty_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1)
+                        subgroup_uncertainty_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1)
+                    with gr.Row():
+                        positive_rate_metric_vw4 = gr.Dropdown(
+                            [POSITIVE_RATE],
+                            value=POSITIVE_RATE, multiselect=False, label="Positive-Rate Metric",
+                            scale=2
+                        )
+                        positive_rate_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1)
+                        positive_rate_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1)
+
+                    btn_view4 = gr.Button("Submit")
+                with gr.Column():
+                    gr.Markdown(
+                        """
+                        ### Disparity Metric Constraints
+                        """)
+                    with gr.Row():
+                        fairness_metric_vw4 = gr.Dropdown(
+                            sorted(self.all_error_disparity_metrics),
+                            value=EQUALIZED_ODDS_FPR, multiselect=False, label="Error Disparity Metric",
+                            scale=2
+                        )
+                        fairness_min_val_vw4 = gr.Text(value="-1.0", label="Min value", scale=1)
+                        fairness_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1)
+                    with gr.Row():
+                        group_stability_metrics_vw4 = gr.Dropdown(
+                            sorted(self.all_stability_disparity_metrics),
+                            value=LABEL_STABILITY_RATIO, multiselect=False, label="Stability Disparity Metric",
+                            scale=2
+                        )
+                        group_stab_min_val_vw4 = gr.Text(value="0.7", label="Min value", scale=1)
+                        group_stab_max_val_vw4 = gr.Text(value="1.5", label="Max value", scale=1)
+                    with gr.Row():
+                        group_uncertainty_metrics_vw4 = gr.Dropdown(
+                            sorted(self.all_uncertainty_disparity_metrics),
+                            value=ALEATORIC_UNCERTAINTY_PARITY, multiselect=False, label="Uncertainty Disparity Metric",
+                            scale=2
+                        )
+                        group_uncertainty_min_val_vw4 = gr.Text(value="-1.0", label="Min value", scale=1)
+                        group_uncertainty_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1)
+                    with gr.Row():
+                        group_positive_rate_metrics_vw4 = gr.Dropdown(
+                            sorted([DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE]),
+                            value=DISPARATE_IMPACT, multiselect=False, label="Positive-Rate Disparity Metric",
+                            scale=2
+                        )
+                        group_positive_rate_min_val_vw4 = gr.Text(value="0.7", label="Min value", scale=1)
+                        group_positive_rate_max_val_vw4 = gr.Text(value="1.5", label="Max value", scale=1)
+            with gr.Row():
+                model_performance_summary = gr.Plot(label="Model Performance Summary")
+
+            btn_view4.click(self._create_model_performance_summary,
+                            inputs=[model_name_vw4,
+                                    accuracy_metric_vw4, accuracy_min_val_vw4, accuracy_max_val_vw4,
+                                    subgroup_stability_metric_vw4, subgroup_stab_min_val_vw4, subgroup_stab_max_val_vw4,
+                                    subgroup_uncertainty_metric_vw4, subgroup_uncertainty_min_val_vw4, subgroup_uncertainty_max_val_vw4,
+                                    positive_rate_metric_vw4, positive_rate_min_val_vw4, positive_rate_max_val_vw4,
+                                    fairness_metric_vw4, fairness_min_val_vw4, fairness_max_val_vw4,
+                                    group_stability_metrics_vw4, group_stab_min_val_vw4, group_stab_max_val_vw4,
+                                    group_uncertainty_metrics_vw4, group_uncertainty_min_val_vw4, group_uncertainty_max_val_vw4,
+                                    group_positive_rate_metrics_vw4, group_positive_rate_min_val_vw4, group_positive_rate_max_val_vw4],
+                            outputs=[model_performance_summary])
+
+        self.demo = demo
+        self.demo.launch(inline=False, debug=True, show_error=True)
+
+    def stop_web_app(self):
+        self.demo.close()
+
+    def __filter_subgroup_metrics_df(self, results: dict, subgroup_metric: str,
+                                     selected_metric: str, selected_subgroup: str, defined_model_names: list):
+        results[subgroup_metric] = dict()
+
+        # Get distinct sorted model names
+        sorted_model_names_arr = self.sorted_model_metrics_df[
+            (self.sorted_model_metrics_df.Metric == selected_metric) &
+            (self.sorted_model_metrics_df.Subgroup == selected_subgroup)
+            ]['Model_Name'].values
+        sorted_model_names_arr = [model for model in sorted_model_names_arr if model in defined_model_names]
+
+        # Add values to a results dict
+        for idx, model_name in enumerate(sorted_model_names_arr):
+            metric_value = self.sorted_model_metrics_df[
+                (self.sorted_model_metrics_df.Metric == selected_metric) &
+                (self.sorted_model_metrics_df.Subgroup == selected_subgroup) &
+                (self.sorted_model_metrics_df.Model_Name == model_name)
+                ]['Value'].values[0]
+            metric_value = metric_value
+            results[subgroup_metric][model_name] = metric_value
+
+        return results
+
+    def __check_metric_constraints(self, model_performance_dct, input_constraint_dct):
+        model_metrics_constraints_check_dct = dict()
+        for metric_dim in model_performance_dct.keys():
+            model_metrics_constraints_check_dct[metric_dim] = dict()
+            for group in model_performance_dct[metric_dim]:
+                constraint_type = 'overall' if group == 'Overall' else 'disparity'
+                min_val, max_val = input_constraint_dct[metric_dim][constraint_type][1]
+                if min_val > max_val:
+                    raise ValueError(f'Max value for the {metric_dim} {constraint_type} dimension should be greater than min value.')
+
+                check = 1 if model_performance_dct[metric_dim][group] >= min_val and model_performance_dct[metric_dim][group] <= max_val else 0
+                model_metrics_constraints_check_dct[metric_dim][group] = check
+
+        return model_metrics_constraints_check_dct
+
+    def _create_dataset_proportions_bar_chart(self, grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8,
+                                              grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8):
+        grp_names = [grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8]
+        grp_names = [grp.strip() for grp in grp_names if grp != '' and grp is not None]
+        grp_dis_values = [grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8]
+        grp_dis_values = [grp.strip() for grp in grp_dis_values if grp != '' and grp is not None]
+
+        if len(grp_names) != len(grp_dis_values):
+            raise ValueError("Numbers of sensitive attributes and their disadvantaged groups are different."
+                             "Please, put '-' as a disadvantaged value for intersectional sensitive attributes.")
+
+        # Create a sensitive attrs dict
+        input_sensitive_attrs_dct = dict()
+        for grp_name, grp_dis_val in zip(grp_names, grp_dis_values):
+            if '&' in grp_name:
+                input_sensitive_attrs_dct[grp_name] = None
+            else:
+                try:
+                    converted_grp_dis_val = eval(grp_dis_val)
+                    input_sensitive_attrs_dct[grp_name] = converted_grp_dis_val
+                except Exception as _:
+                    raise ValueError(f"Type casting error with the {grp_dis_val} value. Use quotes for string disavantaged values.")
+
+        # Partition on protected groups
+        protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)
+
+        # Create a df with group proportions and group base rates
+        subgroup_proportions_dct = compute_proportions(protected_groups, self.X_data)
+        subgroup_base_rates_dct = compute_base_rates(protected_groups, self.y_data)
+        subgroup_relative_base_rates_dct = dict()
+        for subgroup in subgroup_proportions_dct.keys():
+            pct = subgroup_base_rates_dct[subgroup]['percentage'] * subgroup_proportions_dct[subgroup]['percentage']
+            subgroup_relative_base_rates_dct[subgroup] = {'percentage': pct, 'num_rows': subgroup_base_rates_dct[subgroup]['num_rows']}
+
+        stats_df = pd.DataFrame(columns=['Subgroup', 'Percentage', 'Num_Rows', 'Statistics_Type'])
+        for subgroup in subgroup_proportions_dct.keys():
+            stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_proportions_dct[subgroup]['percentage'],
+                                                 subgroup_proportions_dct[subgroup]['num_rows'], 'Proportion']
+            stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_relative_base_rates_dct[subgroup]['percentage'],
+                                                 subgroup_relative_base_rates_dct[subgroup]['num_rows'], 'Base_Rate']
+
+        # Create a row facet bar chart
+        facet_sort_by_lst = ['overall'] + [grp for grp in stats_df.Subgroup.unique() if grp.lower() != 'overall']
+        col_facet_bar_chart = create_col_facet_bar_chart(stats_df,
+                                                         x_col='Statistics_Type',
+                                                         y_col='Num_Rows',
+                                                         facet_column_name='Subgroup',
+                                                         text_labels_column='Percentage',
+                                                         x_sort_by_lst=['Proportion', 'Base_Rate'],
+                                                         facet_sort_by_lst=facet_sort_by_lst,
+                                                         color_legend_title='Statistics Type',
+                                                         facet_title='')
+
+        return col_facet_bar_chart
+
+    def _create_bar_plot_for_model_selection(self, group_name, overall_metric1, overall_metric_min_val1, overall_metric_max_val1,
+                                             disparity_metric1, disparity_metric_min_val1, disparity_metric_max_val1,
+                                             overall_metric2, overall_metric_min_val2, overall_metric_max_val2,
+                                             disparity_metric2, disparity_metric_min_val2, disparity_metric_max_val2):
+        metric_name_to_alias_dct = {
+            overall_metric1: 'C1',
+            disparity_metric1: 'C2',
+            overall_metric2: 'C3',
+            disparity_metric2: 'C4',
+        }
+        overall_constraint1 = (overall_metric1, str_to_float(overall_metric_min_val1, 'Overall Constraint (C1) min value'), str_to_float(overall_metric_max_val1, 'Overall Constraint (C1) max value'))
+        disparity_constraint1 = (disparity_metric1, str_to_float(disparity_metric_min_val1, 'Disparity Constraint (C2) min value'), str_to_float(disparity_metric_max_val1, 'Disparity Constraint (C2) max value'))
+        overall_constraint2 = (overall_metric2, str_to_float(overall_metric_min_val2, 'Overall Constraint (C3) min value'), str_to_float(overall_metric_max_val2, 'Overall Constraint (C3) max value'))
+        disparity_constraint2 = (disparity_metric2, str_to_float(disparity_metric_min_val2, 'Disparity Constraint (C4) min value'), str_to_float(disparity_metric_max_val2, 'Disparity Constraint (C4) max value'))
+
+        # Create individual constraints
+        metrics_value_range_dct = dict()
+        for constraint in [overall_constraint1, disparity_constraint1, overall_constraint2, disparity_constraint2]:
+            metrics_value_range_dct[constraint[0]] = [constraint[1], constraint[2]]
+        # Create intersectional constraints
+        metrics_value_range_dct[f'{overall_constraint1[0]}&{disparity_constraint1[0]}'] = None
+        metrics_value_range_dct[f'{overall_constraint1[0]}&{overall_constraint2[0]}'] = None
+        metrics_value_range_dct[f'{overall_constraint1[0]}&{disparity_constraint2[0]}'] = None
+        metrics_value_range_dct[(f'{overall_constraint1[0]}&{disparity_constraint1[0]}'
+                                 f'&{overall_constraint2[0]}&{disparity_constraint2[0]}')] = None
+
+        melted_all_subgroup_metrics_per_model_dct = dict()
+        for model_name in self.melted_model_metrics_df['Model_Name'].unique():
+            melted_all_subgroup_metrics_per_model_dct[model_name] = (
+                self.melted_model_metrics_df)[self.melted_model_metrics_df.Model_Name == model_name]
+
+        melted_all_group_metrics_per_model_dct = dict()
+        for model_name in self.melted_model_composed_metrics_df['Model_Name'].unique():
+            melted_all_group_metrics_per_model_dct[model_name] = (
+                self.melted_model_composed_metrics_df)[self.melted_model_composed_metrics_df.Model_Name == model_name]
+
+        return create_flexible_bar_plot_for_model_selection(melted_all_subgroup_metrics_per_model_dct,
+                                                            melted_all_group_metrics_per_model_dct,
+                                                            metrics_value_range_dct,
+                                                            group=group_name,
+                                                            metric_name_to_alias_dct=metric_name_to_alias_dct)
+
+    def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accuracy_metrics_lst: list,
+                                            subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list,
+                                            tolerance: str):
+        """
+        Create a group model rank heatmap.
+
+        Parameters
+        ----------
+        model_names
+            A list of selected model names to display on the heatmap
+        subgroup_accuracy_metrics_lst
+            A list of subgroup accuracy metrics to visualize
+        subgroup_uncertainty_metrics
+            A list of subgroup uncertainty metrics to visualize
+        subgroup_stability_metrics_lst
+            A list of subgroup stability metrics to visualize
+        tolerance
+            An acceptable value difference for metrics dense ranking
+
+        """
+        tolerance = str_to_float(tolerance, 'Tolerance')
+        if tolerance < 0.001 or tolerance > 0.2:
+            raise ValueError('Tolerance should be in the [0.001, 0.2] range')
+        metrics_lst = subgroup_accuracy_metrics_lst + subgroup_uncertainty_metrics + subgroup_stability_metrics_lst
+
+        # Find metric values for each model based on metric, subgroup, and model names.
+        # Add the values to a results dict.
+        results = {}
+        for metric in metrics_lst:
+            # Add an overall metric
+            subgroup_metric = metric
+            results = self.__filter_subgroup_metrics_df(results, subgroup_metric, metric,
+                                                        selected_subgroup='overall', defined_model_names=model_names)
+
+        model_metrics_matrix = pd.DataFrame(results).T
+        model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)]
+        model_metrics_matrix = model_metrics_matrix.round(3)  # round to make tolerance more precise
+        sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance)
+        model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank)
+
+        return model_rank_heatmap
+
+    def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_metrics_lst: list,
+                                         group_uncertainty_metrics: list, group_stability_metrics_lst: list, tolerance: str):
+        """
+        Create a group model rank heatmap.
+
+        Parameters
+        ----------
+        model_names
+            A list of selected model names to display on the heatmap
+        group_fairness_metrics_lst
+            A list of group fairness metrics to visualize
+        group_uncertainty_metrics
+            A list of group uncertainty metrics to visualize
+        group_stability_metrics_lst
+            A list of group stability metrics to visualize
+        tolerance
+            An acceptable value difference for metrics dense ranking
+
+        """
+        tolerance = str_to_float(tolerance, 'Tolerance')
+        if tolerance < 0.001 or tolerance > 0.2:
+            raise ValueError('Tolerance should be in the [0.001, 0.2] range')
+
+        groups_lst = self.sensitive_attributes_dct.keys()
+        metrics_lst = group_fairness_metrics_lst + group_uncertainty_metrics + group_stability_metrics_lst
+
+        # Find metric values for each model based on metric, group, and model names.
+        # Add the values to a results dict.
+        results = {}
+        for metric in metrics_lst:
+            for group in groups_lst:
+                group_metric = metric + '_' + group
+                results[group_metric] = dict()
+
+                # Get distinct sorted model names
+                sorted_model_names_arr = self.sorted_model_composed_metrics_df[
+                    (self.sorted_model_composed_metrics_df.Metric == metric) &
+                    (self.sorted_model_composed_metrics_df.Subgroup == group)
+                    ]['Model_Name'].values
+                sorted_model_names_arr = [model for model in sorted_model_names_arr if model in model_names]
+
+                # Add values to a results dict
+                for idx, model_name in enumerate(sorted_model_names_arr):
+                    metric_value = self.sorted_model_composed_metrics_df[
+                        (self.sorted_model_composed_metrics_df.Metric == metric) &
+                        (self.sorted_model_composed_metrics_df.Subgroup == group) &
+                        (self.sorted_model_composed_metrics_df.Model_Name == model_name)
+                        ]['Value'].values[0]
+                    metric_value = metric_value
+                    results[group_metric][model_name] = metric_value
+
+        model_metrics_matrix = pd.DataFrame(results).T
+        model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)]
+        model_metrics_matrix = model_metrics_matrix.round(3)  # round to make tolerance more precise
+        sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix, tolerance)
+        model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank)
+
+        return model_rank_heatmap
+
+    def _create_model_performance_summary(self, model_name: str, accuracy_metric, accuracy_min_val, accuracy_max_val,
+                                          stability_metric, stability_min_val, stability_max_val,
+                                          uncertainty_metric, uncertainty_min_val, uncertainty_max_val,
+                                          positive_rate_metric, positive_rate_min_val, positive_rate_max_val,
+                                          fairness_metric, fairness_min_val, fairness_max_val,
+                                          group_stability_metric, group_stab_min_val, group_stab_max_val,
+                                          group_uncertainty_metric, group_uncertainty_min_val, group_uncertainty_max_val,
+                                          group_positive_rate_metric, group_positive_rate_min_val, group_positive_rate_max_val):
+        accuracy_constraint = (accuracy_metric, [str_to_float(accuracy_min_val, 'Accuracy min value'),
+                                                 str_to_float(accuracy_max_val, 'Accuracy max value')])
+        stability_constraint = (stability_metric, [str_to_float(stability_min_val, 'Stability min value'),
+                                                   str_to_float(stability_max_val, 'Stability max value')])
+        uncertainty_constraint = (uncertainty_metric, [str_to_float(uncertainty_min_val, 'Uncertainty min value'),
+                                                       str_to_float(uncertainty_max_val, 'Uncertainty max value')])
+        positive_rate_constraint = (positive_rate_metric, [str_to_float(positive_rate_min_val, 'Positive-Rate min value'),
+                                                           str_to_float(positive_rate_max_val, 'Positive-Rate max value')])
+
+        fairness_constraint = (fairness_metric, [str_to_float(fairness_min_val, 'Error disparity metric min value'),
+                                                 str_to_float(fairness_max_val, 'Error disparity metric max value')])
+        group_stability_constraint = (group_stability_metric, [str_to_float(group_stab_min_val, 'Stability disparity min value'),
+                                                               str_to_float(group_stab_max_val, 'Stability disparity max value')])
+        group_uncertainty_constraint = (group_uncertainty_metric, [str_to_float(group_uncertainty_min_val, 'Uncertainty disparity min value'),
+                                                                   str_to_float(group_uncertainty_max_val, 'Uncertainty disparity max value')])
+        group_positive_rate_constraint = (group_positive_rate_metric, [str_to_float(group_positive_rate_min_val, 'Positive-Rate disparity min value'),
+                                                                       str_to_float(group_positive_rate_max_val, 'Positive-Rate disparity max value')])
+
+        input_constraints_dct = {
+            'Accuracy': {
+                'overall': accuracy_constraint,
+                'disparity': fairness_constraint,
+            },
+            'Stability': {
+                'overall': stability_constraint,
+                'disparity': group_stability_constraint,
+            },
+            'Uncertainty': {
+                'overall': uncertainty_constraint,
+                'disparity': group_uncertainty_constraint,
+            },
+            'Positive-Rate': {
+                'overall': positive_rate_constraint,
+                'disparity': group_positive_rate_constraint,
+            },
+        }
+
+        # Extract overall and disparity metrics from metrics dfs.
+        # Add the values to a results dict.
+        model_performance_dct = {}
+        for metric_dim in input_constraints_dct.keys():
+            model_performance_dct[metric_dim] = dict()
+            subgroup_metric = input_constraints_dct[metric_dim]['overall'][0]
+            model_performance_dct[metric_dim]['Overall'] = self.sorted_model_metrics_df[
+                (self.sorted_model_metrics_df.Metric == subgroup_metric) &
+                (self.sorted_model_metrics_df.Subgroup == 'overall') &
+                (self.sorted_model_metrics_df.Model_Name == model_name)
+                ]['Value'].values[0]
+
+            group_metric = input_constraints_dct[metric_dim]['disparity'][0]
+            for group_name in self.group_names:
+                model_performance_dct[metric_dim]['Disparity: ' + group_name] = self.sorted_model_composed_metrics_df[
+                    (self.sorted_model_composed_metrics_df.Metric == group_metric) &
+                    (self.sorted_model_composed_metrics_df.Subgroup == group_name) &
+                    (self.sorted_model_composed_metrics_df.Model_Name == model_name)
+                    ]['Value'].values[0]
+
+        metric_constraints_check_dct = self.__check_metric_constraints(model_performance_dct, input_constraints_dct)
+
+        model_metrics_matrix = pd.DataFrame(model_performance_dct).T
+        aligned_column_names = ['Overall'] + [col for col in model_metrics_matrix.columns if col != 'Overall']
+        model_metrics_matrix = model_metrics_matrix[aligned_column_names]
+        metric_constraints_check_matrix = pd.DataFrame(metric_constraints_check_dct).T
+        metric_constraints_check_matrix = metric_constraints_check_matrix[aligned_column_names]
+
+        model_performance_summary, _ = create_model_performance_summary_visualization(model_metrics_matrix, metric_constraints_check_matrix)
+        return model_performance_summary
+
+    def _create_subgroup_metrics_bar_chart_per_one_model(self, model_name: str, subgroup_accuracy_metrics_lst: list,
+                                                         subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list):
+        metrics_names = subgroup_accuracy_metrics_lst + subgroup_uncertainty_metrics + subgroup_stability_metrics_lst
+        return self._create_metrics_bar_chart_per_one_model(model_name, metrics_names, metrics_type='subgroup')
+
+    def _create_group_metrics_bar_chart_per_one_model(self, model_name: str, group_fairness_metrics_lst: list,
+                                                      group_uncertainty_metrics_lst: list,
+                                                      group_stability_metrics_lst: list):
+        metrics_names = group_fairness_metrics_lst + group_uncertainty_metrics_lst + group_stability_metrics_lst
+        return self._create_metrics_bar_chart_per_one_model(model_name, metrics_names, metrics_type='group')
+
+    def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names: list, metrics_type: str):
+        """
+        This bar chart displays metrics for different groups and one specific model.
+
+        Parameters
+        ----------
+        model_name
+            A model name to display metrics
+        metrics_names
+            A list of metric names to visualize
+        metrics_type
+            A metrics type ('subgroup' or 'group') to visualize
+
+        """
+        metrics_title = 'Disparity Metrics' if metrics_type == "group" else 'Group Specific Metrics'
+        metrics_df = self.melted_model_composed_metrics_df if metrics_type == "group" else self.melted_model_metrics_df
+        filtered_groups = [grp for grp in metrics_df.Subgroup.unique() if '_correct' not in grp and '_incorrect' not in grp]
+        filtered_groups = [grp for grp in filtered_groups if grp.lower() != 'overall'] + ['overall']
+        filtered_metrics_df = metrics_df[(metrics_df['Metric'].isin(metrics_names)) &
+                                         (metrics_df['Model_Name'] == model_name) &
+                                         (metrics_df['Subgroup'].isin(filtered_groups))]
+
+        base_font_size = 16
+        models_metrics_chart = (
+            alt.Chart().mark_bar().encode(
+                alt.Y('Subgroup:N', axis=None, sort=filtered_groups),
+                alt.X('Value:Q', axis=alt.Axis(grid=True), title=''),
+                alt.Color('Subgroup:N',
+                          scale=alt.Scale(scheme="tableau20"),
+                          sort=filtered_groups,
+                          legend=alt.Legend(title='Disparity' if metrics_type == 'group' else 'Group',
+                                            labelFontSize=base_font_size,
+                                            titleFontSize=base_font_size + 2))
+            )
+        )
+
+        text = (
+            models_metrics_chart.mark_text(
+                align='left',
+                baseline='middle',
+                fontSize=base_font_size,
+                dx=10
+            ).encode(
+                text=alt.Text('Value:Q', format=",.3f"),
+                color=alt.value("black")
+            )
+        )
+
+        final_chart = (
+            alt.layer(
+                models_metrics_chart, text, data=filtered_metrics_df
+            ).properties(
+                width=500,
+                height=100
+            ).facet(
+                row=alt.Row('Metric:N', title=metrics_title, sort=metrics_names)
+            ).configure(
+                padding={'top':  33},
+            ).configure_headerRow(
+                labelAngle=0,
+                labelPadding=10,
+                labelAlign='left',
+                labelFontSize=base_font_size,
+                titleFontSize=base_font_size + 2
+            ).configure_axis(
+                labelFontSize=base_font_size, titleFontSize=base_font_size + 2
+            )
+        )
+
+        return final_chart
diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py
index e895b493..377bcdf9 100644
--- a/virny/custom_classes/metrics_visualizer.py
+++ b/virny/custom_classes/metrics_visualizer.py
@@ -8,6 +8,7 @@
 from datetime import datetime, timezone
 
 from virny.configs.constants import ReportType
+from virny.utils.data_viz_utils import create_sorted_matrix_by_rank
 
 
 class MetricsVisualizer:
@@ -252,19 +253,6 @@ def create_fairness_variance_interactive_bar_chart(self):
                 )
         )
 
-    @staticmethod
-    def _create_sorted_matrix_by_rank(model_metrics_matrix) -> np.array:
-        models_distances_matrix = model_metrics_matrix.copy(deep=True).T
-        metric_names = models_distances_matrix.columns
-        for metric_name in metric_names:
-            if 'impact' in metric_name.lower() or 'ratio' in metric_name.lower():
-                models_distances_matrix[metric_name] = models_distances_matrix[metric_name] - 1
-            models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs()
-
-        models_distances_matrix = models_distances_matrix.T
-        sorted_matrix_by_rank = np.argsort(np.argsort(models_distances_matrix, axis=1), axis=1)
-        return sorted_matrix_by_rank
-
     def create_model_rank_heatmap(self, model_metrics_matrix, sorted_matrix_by_rank, num_models: int):
         """
         This heatmap includes all group fairness and stability metrics and all defined models.
@@ -385,7 +373,7 @@ def create_model_rank_heatmaps(self, metrics_lst: list, groups_lst):
                     results[group_metric][model_name] = metric_value
 
         model_metrics_matrix = pd.DataFrame(results).T
-        sorted_matrix_by_rank = MetricsVisualizer._create_sorted_matrix_by_rank(model_metrics_matrix)
+        sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix)
         model_rank_heatmap = self.create_model_rank_heatmap(model_metrics_matrix, sorted_matrix_by_rank, num_models)
         total_model_rank_heatmap = self.create_total_model_rank_heatmap(sorted_matrix_by_rank, num_models)
         if self.__create_report:
diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py
index dc9e81ab..d82b4944 100644
--- a/virny/utils/common_helpers.py
+++ b/virny/utils/common_helpers.py
@@ -1,4 +1,5 @@
 import os
+import re
 
 from datetime import datetime, timezone
 from sklearn.metrics import confusion_matrix
@@ -71,6 +72,20 @@ def validate_config(config_obj):
     return True
 
 
+def isfloat_regex(string):
+    # We have defined a pattern for float value
+    pattern = r'^[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?$'
+    # Find the match and convert to boolean
+    return bool(re.match(pattern, string))
+
+
+def str_to_float(str_var: str, var_name: str):
+    if isfloat_regex(str_var):
+        return float(str_var)
+    else:
+        raise ValueError(f"{var_name} must be a float number with a '.' separator.")
+
+
 def reset_model_seed(model, new_seed, verbose):
     if isinstance(model, base.Classifier): # For incremental models
         model.seed = new_seed
@@ -93,6 +108,15 @@ def save_metrics_to_file(metrics_df, result_filename, save_dir_path):
     metrics_df.to_csv(f'{save_dir_path}/{filename}', index=False)
 
 
+def check_substring_in_list(val_to_check: str, allowed_lst: list):
+    # Case-insensitive check if a val_to_check substring is in allowed_lst
+    val_to_check = val_to_check.lower()
+    for allowed_val in allowed_lst:
+        if allowed_val.lower() in val_to_check:
+            return True
+    return False
+
+
 def confusion_matrix_metrics(y_true, y_preds):
     metrics = dict()
     TN, FP, FN, TP = confusion_matrix(y_true, y_preds).ravel()
diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py
index bb262b3a..8cae6f28 100644
--- a/virny/utils/data_viz_utils.py
+++ b/virny/utils/data_viz_utils.py
@@ -1,36 +1,526 @@
-import os
+import math
+import numpy as np
 import pandas as pd
+import altair as alt
+import seaborn as sns
 
+from matplotlib import pyplot as plt
+from altair.utils.schemapi import Undefined
 
-def create_average_metrics_df(dataset_name, model_names, metrics_path):
-    results_filenames = [filename for filename in os.listdir(metrics_path)]
-    models_average_results_dct = dict()
-    for model_name in model_names:
-        model_results_filenames = [filename for filename in results_filenames if 'Average_Metrics' not in filename
-                                   and dataset_name in filename
-                                   and model_name in filename]
+from virny.utils.common_helpers import check_substring_in_list
+from IPython.display import display
 
-        if len(model_results_filenames) == 0:
-            continue
 
-        model_results_dfs = []
-        for model_results_filename in model_results_filenames:
-            model_results_df = pd.read_csv(f'{metrics_path}/{model_results_filename}')
-            model_results_df.set_index('index', inplace = True)
-            model_results_dfs.append(model_results_df)
+def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.001):
+    """
+    Rank a pandas series with defined tolerance.
+    Ref: https://stackoverflow.com/questions/72956450/pandas-ranking-with-tolerance
 
-        model_average_results_df = None
-        for model_results_df in model_results_dfs:
-            if model_average_results_df is None:
-                model_average_results_df = model_results_df
-            else:
-                model_average_results_df += model_results_df
+    Parameters
+    ----------
+    pd_series
+        A pandas series to rank
+    tolerance
+        A float value for ranking
 
-        model_average_results_df = model_average_results_df / len(model_results_dfs)
-        models_average_results_dct[model_name] = model_average_results_df
+    Returns
+    -------
+    A pandas series with dense ranks for the input pd series.
 
-        filename = f'Average_Metrics_{dataset_name}_{model_name}.csv'
-        model_average_results_df.reset_index().to_csv(f'{metrics_path}/{filename}', index=False)
-        print(f'File with average metrics for {model_name} is created')
+    """
+    sorted_vals = sorted(pd_series.tolist())
 
-    return models_average_results_dct
+    # Create a dictionary with bin constraints
+    bin_constraints_dct = dict()
+    for i in range(len(sorted_vals)):
+        val = sorted_vals[i]
+        rank = i + 1
+        bin_constraints_dct[rank] = [round(val - tolerance, 3), round(val + tolerance, 3)]
+
+    # Assign ranks for each pandas series value
+    assigned_ranks_dct = dict()
+    for i in range(len(sorted_vals)):
+        val = sorted_vals[i]
+        max_rank = i + 1
+        actual_rank = None
+        for rank in bin_constraints_dct.keys():
+            min_limit, max_limit = bin_constraints_dct[rank]
+            if min_limit <= val <= max_limit:
+                actual_rank = rank
+                break
+
+        assigned_ranks_dct[str(round(val, 3))] = actual_rank
+        # Dynamically delete constraints from bin_constraints_dct to keep values in the same bin with tolerance
+        if actual_rank != max_rank:
+            del bin_constraints_dct[max_rank]
+
+    def get_rank_with_tolerance(val):
+        return assigned_ranks_dct[str(round(val, 3))]
+
+    return pd_series.apply(get_rank_with_tolerance).rank(method='dense')
+
+
+def compute_proportions(protected_groups, X_data):
+    subgroup_proportions_dct = {'overall': {'percentage': 1.0, 'num_rows': X_data.shape[0]}}
+    for col_name in protected_groups.keys():
+        proportion = protected_groups[col_name].shape[0] / X_data.shape[0]
+        subgroup_proportions_dct[col_name] = {'percentage': proportion, 'num_rows': protected_groups[col_name].shape[0]}
+
+    return subgroup_proportions_dct
+
+
+def compute_base_rates(protected_groups, y_data):
+    overall_base_rate = y_data[y_data == 1].shape[0] / y_data.shape[0]
+    subgroup_base_rates_dct = {'overall': {'percentage': overall_base_rate, 'num_rows': y_data[y_data == 1].shape[0]}}
+    for col_name in protected_groups.keys():
+        filtered_df = y_data.iloc[protected_groups[col_name].index].copy(deep=True)
+        base_rate = filtered_df[filtered_df == 1].shape[0] / filtered_df.shape[0]
+        subgroup_base_rates_dct[col_name] = {'percentage': base_rate, 'num_rows': filtered_df[filtered_df == 1].shape[0]}
+
+    return subgroup_base_rates_dct
+
+
+def create_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np.array:
+    models_distances_matrix = model_metrics_matrix.copy(deep=True).T
+    metric_names = models_distances_matrix.columns
+    for metric_name in metric_names:
+        if 'impact' in metric_name.lower() or 'ratio' in metric_name.lower():
+            models_distances_matrix[metric_name] = models_distances_matrix[metric_name] - 1
+        models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs()
+
+    models_distances_matrix = models_distances_matrix.T
+    models_distances_df = pd.DataFrame(models_distances_matrix)
+    sorted_matrix_by_rank = models_distances_df.apply(
+        lambda row : rank_with_tolerance(row, tolerance), axis = 1
+    )
+
+    return sorted_matrix_by_rank
+
+
+def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np.array:
+    models_distances_matrix = model_metrics_matrix.copy(deep=True).T
+    metric_names = models_distances_matrix.columns
+    for metric_name in metric_names:
+        if check_substring_in_list(metric_name, ['TPR', 'TNR', 'PPV', 'Accuracy', 'F1', 'Label_Stability', 'Positive-Rate']):
+            # Cast a metric to a case when the closer value to one is the better
+            models_distances_matrix[metric_name] = 1 - models_distances_matrix[metric_name]
+        models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs()
+
+    models_distances_matrix = models_distances_matrix.T
+    models_distances_df = pd.DataFrame(models_distances_matrix)
+    sorted_matrix_by_rank = models_distances_df.apply(
+        lambda row : rank_with_tolerance(row, tolerance), axis = 1
+    )
+
+    return sorted_matrix_by_rank
+
+
+def create_col_facet_bar_chart(df, x_col, y_col, facet_column_name, text_labels_column, x_sort_by_lst=Undefined,
+                               facet_sort_by_lst=Undefined, color_legend_title=Undefined, facet_title=Undefined):
+    num_facets = len(df[facet_column_name].unique())
+    max_y_axis_limit = df[y_col].max()
+    base_font_size = 16
+
+    # Set dynamic variables that adapt to the number of defined groups
+    dynamic_facet_width = 100
+    dynamic_label_angle = -20
+    dynamic_font_size = base_font_size
+    dynamic_top_padding = 40
+    dynamic_legend_y_padding = -140
+    if num_facets > 4 * 2 + 1 and num_facets <= 6 * 2 + 1:
+        dynamic_facet_width = 75
+        dynamic_label_angle = -25
+        dynamic_font_size -= 2
+        dynamic_top_padding = 40
+        dynamic_legend_y_padding = -160
+    elif num_facets > 6 * 2 + 1:
+        dynamic_facet_width = 50
+        dynamic_label_angle = -45
+        dynamic_font_size -= 4
+        dynamic_top_padding = 50
+        dynamic_legend_y_padding = -200
+
+    bar_chart = (
+        alt.Chart().mark_bar().encode(
+            alt.X(f'{x_col}:N', axis=None, sort=x_sort_by_lst),
+            alt.Y(f'{y_col}:Q', axis=alt.Axis(grid=True), title='', scale=alt.Scale(domain=[0, max_y_axis_limit])),
+            alt.Color(f'{x_col}:N',
+                      scale=alt.Scale(scheme="tableau20"),
+                      sort=x_sort_by_lst,
+                      legend=alt.Legend(title=color_legend_title,
+                                        labelFontSize=base_font_size,
+                                        titleFontSize=base_font_size + 2,
+                                        orient='none',
+                                        legendX=0, legendY=dynamic_legend_y_padding,
+                                        direction='horizontal'))
+        )
+    )
+
+    text_labels = (
+        bar_chart.mark_text(
+            baseline='middle',
+            fontSize=dynamic_font_size,
+            dy=-10
+        ).encode(
+            text=alt.Text(f'{text_labels_column}:Q', format=",.2f"),
+            color=alt.value("black")
+        )
+    )
+
+    final_chart = (
+        alt.layer(
+            bar_chart, text_labels, data=df
+        ).properties(
+            width=dynamic_facet_width,
+            height=500
+        ).facet(
+            column=alt.Column(f'{facet_column_name}:N', title=facet_title,
+                              sort=facet_sort_by_lst, header=alt.Header(labelAngle=dynamic_label_angle,
+                                                                        labelAnchor='middle',
+                                                                        labelAlign='center',
+                                                                        labelPadding=-15))
+        ).configure(
+            padding={'top':  dynamic_top_padding},
+        ).configure_headerColumn(
+            labelFontSize=base_font_size,
+            titleFontSize=base_font_size + 2,
+        ).configure_axis(
+            labelFontSize=base_font_size, titleFontSize=base_font_size + 2
+        )
+    )
+
+    return final_chart
+
+
+def create_row_facet_bar_chart(df, x_col, y_col, facet_column_name, y_sort_by_lst=Undefined,
+                               facet_sort_by_lst=Undefined, color_legend_title=Undefined, facet_title=Undefined):
+    base_font_size = 16
+    bar_chart = (
+        alt.Chart().mark_bar().encode(
+            alt.Y(f'{y_col}:N', axis=None, sort=y_sort_by_lst),
+            alt.X(f'{x_col}:Q', axis=alt.Axis(grid=True), title=''),
+            alt.Color(f'{y_col}:N',
+                      scale=alt.Scale(scheme="tableau20"),
+                      sort=y_sort_by_lst,
+                      legend=alt.Legend(title=color_legend_title,
+                                        labelFontSize=base_font_size,
+                                        titleFontSize=base_font_size + 2,
+                                        orient='top'))
+        )
+    )
+
+    text_labels = (
+        bar_chart.mark_text(
+            baseline='middle',
+            fontSize=base_font_size,
+            dx=10
+        ).encode(
+            text=alt.Text('Value:Q', format=",.3f"),
+            color=alt.value("black")
+        )
+    )
+
+    final_chart = (
+        alt.layer(
+            bar_chart, text_labels, data=df
+        ).properties(
+            width=500,
+            height=100
+        ).facet(
+            row=alt.Row(f'{facet_column_name}:N', title=facet_title, sort=facet_sort_by_lst)
+        ).configure(
+            padding={'top':  33},
+        ).configure_headerRow(
+            labelFontSize=base_font_size,
+            titleFontSize=base_font_size + 2
+        ).configure_axis(
+            labelFontSize=base_font_size, titleFontSize=base_font_size + 2
+        )
+    )
+
+    return final_chart
+
+
+def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank):
+    """
+    This heatmap includes group fairness and stability metrics and defined models.
+    Using it, you can visually compare the models across defined group metrics. On this plot,
+    colors display ranks where 1 is the best model for the metric. These ranks are conditioned
+    on difference or ratio operations used to create these group metrics:
+
+    1) if the metric is created based on the difference operation, closer values to zero have ranks that are closer to the first rank
+
+    2) if the metric is created based on the ratio operation, closer values to one have ranks that are closer to the first rank
+
+    Parameters
+    ----------
+    model_metrics_matrix
+        Matrix of model metrics values where indexes are group metric names and columns are model names
+    sorted_matrix_by_rank
+        Matrix of model ranks per metric where indexes are group metric names and columns are model names
+
+    """
+    font_increase = 4
+    matrix_width = 20
+    matrix_height = model_metrics_matrix.shape[0] if model_metrics_matrix.shape[0] >= 3 \
+        else model_metrics_matrix.shape[0] * 2.5
+    num_ranks = int(sorted_matrix_by_rank.values.max())
+
+    fig = plt.figure(figsize=(matrix_width, matrix_height))
+    # Set a green color when there is only one rank
+    if num_ranks == 1:
+        rank_colors = sns.diverging_palette(145, 13, s=75, l=70, n=num_ranks).as_hex()
+    else:
+        rank_colors = sns.diverging_palette(13, 145, s=75, l=70, n=num_ranks).as_hex()
+    # Convert ranks to minus ranks (1 --> -1; 4 --> -4) to align rank positions with a color scheme
+    reversed_sorted_matrix_by_rank = sorted_matrix_by_rank * -1
+    ax = sns.heatmap(reversed_sorted_matrix_by_rank, annot=model_metrics_matrix.round(3), cmap=rank_colors,
+                     fmt='', annot_kws={'color': 'black', 'alpha': 0.7, 'fontsize': 16 + font_increase})
+    ax.set(xlabel="", ylabel="")
+    ax.xaxis.tick_top()
+    ax.tick_params(axis='x', rotation=10)
+    ax.tick_params(labelsize=16 + font_increase)
+    fig.tight_layout()
+
+    cbar = ax.collections[0].colorbar
+    model_ranks = [idx + 1 for idx in range(num_ranks)]
+    cbar.set_ticks([float(idx) * -1 for idx in model_ranks])
+    tick_labels = ['' for _ in model_ranks]
+    if len(tick_labels) > 1:
+        tick_labels[0] = 'Best'
+        tick_labels[-1] = 'Worst'
+    cbar.set_ticklabels(tick_labels, fontsize=16 + font_increase)
+
+    return fig, ax
+
+
+def create_model_performance_summary_visualization(main_matrix, matrix_for_colors):
+    font_increase = 6
+    matrix_width = 20
+    matrix_height = main_matrix.shape[0] if main_matrix.shape[0] >= 3 else main_matrix.shape[0] * 2.5
+
+    fig = plt.figure(figsize=(matrix_width, matrix_height))
+    ax = sns.heatmap(matrix_for_colors, annot=main_matrix.round(3),
+                     cmap=["#EE8367", "#58D68D"], # [red, green]
+                     fmt='', linewidths=1.0,
+                     vmin=0, vmax=1,
+                     cbar_kws={"ticks":[0, 1]},
+                     annot_kws={'color': 'black', 'alpha': 0.7, 'fontsize': 10 + font_increase})
+    ax.set(xlabel="", ylabel="")
+    ax.xaxis.tick_top()
+    ax.tick_params(axis='y', rotation=0)
+    ax.tick_params(labelsize=10 + font_increase)
+    fig.subplots_adjust(left=0.2, top=0.7)
+
+    cbar = ax.collections[0].colorbar
+    tick_labels = ['Failed', 'Passed']
+    cbar.set_ticks([0.25,0.75])
+    cbar.set_ticklabels(tick_labels, fontsize=10 + font_increase)
+
+    return fig, ax
+
+
+def create_flexible_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict,
+                                                 metrics_value_range_dct: dict, group: str, metric_name_to_alias_dct: dict):
+    # Compute the number of models that satisfy the conditions
+    models_in_range_df, df_with_models_satisfied_all_constraints = (
+        create_models_in_range_dct(all_subgroup_metrics_per_model_dct, all_group_metrics_per_model_dct,
+                                   metrics_value_range_dct, group))
+
+    def get_column_alias(metric_group):
+        if '&' not in metric_group:
+            alias = metric_name_to_alias_dct[metric_group]
+        else:
+            metrics = metric_group.split('&')
+            alias = None
+            for idx, metric in enumerate(metrics):
+                if idx == 0:
+                    alias = metric_name_to_alias_dct[metric]
+                else:
+                    alias += ' & ' + metric_name_to_alias_dct[metric]
+
+        return alias
+
+    # Replace metric groups on their aliases
+    models_in_range_df['Alias'] = models_in_range_df['Metric_Group'].apply(get_column_alias)
+    models_in_range_df['Title'] = models_in_range_df['Alias']
+
+    base_font_size = 14
+    bar_plot = alt.Chart(models_in_range_df).mark_bar().encode(
+        x=alt.X("Title", type="nominal", title='Metric Group', axis=alt.Axis(labelAngle=-30),
+                sort=alt.Sort(order='ascending')),
+        y=alt.Y("Number_of_Models", title="Number of Models", type="quantitative"),
+        color=alt.Color('Model_Type', legend=alt.Legend(title='Model Type'))
+    ).configure(padding={'top':  33}
+                ).configure_axis(
+        labelFontSize=base_font_size + 2,
+        titleFontSize=base_font_size + 4,
+        labelFontWeight='normal',
+        titleFontWeight='normal',
+        labelLimit=300,
+        tickMinStep=1,
+    ).configure_title(
+        fontSize=base_font_size + 2
+    ).configure_legend(
+        titleFontSize=base_font_size + 4,
+        labelFontSize=base_font_size + 2,
+        symbolStrokeWidth=4,
+        labelLimit=300,
+        titleLimit=220,
+    ).properties(width=650, height=450)
+
+    return bar_plot, df_with_models_satisfied_all_constraints
+
+
+def create_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict,
+                                        metrics_value_range_dct: dict, group: str):
+    # Compute the number of models that satisfy the conditions
+    models_in_range_df, df_with_models_satisfied_all_constraints = (
+        create_models_in_range_dct(all_subgroup_metrics_per_model_dct, all_group_metrics_per_model_dct,
+                                   metrics_value_range_dct, group))
+    # Replace metric groups on their aliases
+    metric_name_to_alias_dct = {
+        # C1
+        'TPR': 'C1',
+        'TNR': 'C1',
+        'FNR': 'C1',
+        'FPR': 'C1',
+        'PPV': 'C1',
+        'Accuracy': 'C1',
+        'F1': 'C1',
+        'Positive-Rate': 'C1',
+        # C2
+        'Equalized_Odds_TPR': 'C2',
+        'Equalized_Odds_FPR': 'C2',
+        'Equalized_Odds_FNR': 'C2',
+        'Disparate_Impact': 'C2',
+        'Statistical_Parity_Difference': 'C2',
+        # C3
+        'Std': 'C3',
+        'IQR': 'C3',
+        'Jitter': 'C3',
+        'Label_Stability': 'C3',
+        # C4
+        'IQR_Parity': 'C4',
+        'Label_Stability_Ratio': 'C4',
+        'Label_Stability_Difference': 'C4',
+        'Std_Parity': 'C4',
+        'Std_Ratio': 'C4',
+        'Jitter_Parity': 'C4',
+    }
+
+    def get_column_alias(metric_group):
+        if '&' not in metric_group:
+            alias = metric_name_to_alias_dct[metric_group]
+        else:
+            metrics = metric_group.split('&')
+            alias = None
+            for idx, metric in enumerate(metrics):
+                if idx == 0:
+                    alias = metric_name_to_alias_dct[metric]
+                else:
+                    alias += ' & ' + metric_name_to_alias_dct[metric]
+
+        return alias
+
+    models_in_range_df['Alias'] = models_in_range_df['Metric_Group'].apply(get_column_alias)
+    models_in_range_df['Title'] = models_in_range_df['Alias']
+
+    base_font_size = 14
+    bar_plot = alt.Chart(models_in_range_df).mark_bar().encode(
+        x=alt.X("Title", type="nominal", title='Metric Group', axis=alt.Axis(labelAngle=-30),
+                sort=alt.Sort(order='ascending')),
+        y=alt.Y("Number_of_Models", title="Number of Models", type="quantitative"),
+        color=alt.Color('Model_Type', legend=alt.Legend(title='Model Type'))
+    ).configure(padding={'top':  33}
+    ).configure_axis(
+        labelFontSize=base_font_size + 2,
+        titleFontSize=base_font_size + 4,
+        labelFontWeight='normal',
+        titleFontWeight='normal',
+        labelLimit=300,
+        tickMinStep=1,
+    ).configure_title(
+        fontSize=base_font_size + 2
+    ).configure_legend(
+        titleFontSize=base_font_size + 4,
+        labelFontSize=base_font_size + 2,
+        symbolStrokeWidth=4,
+        labelLimit=300,
+        titleLimit=220,
+    ).properties(width=650, height=450)
+
+    return bar_plot, df_with_models_satisfied_all_constraints
+
+
+def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict,
+                               metrics_value_range_dct: dict, group: str):
+    # Merge subgroup and group metrics for each model and align their columns
+    all_metrics_for_all_models_df = pd.DataFrame()
+    for model_name in all_subgroup_metrics_per_model_dct.keys():
+        group_metrics_per_model_df = all_group_metrics_per_model_dct[model_name][
+            (all_group_metrics_per_model_dct[model_name]['Subgroup'] == group)
+            ]
+        subgroup_metrics_per_model_df = all_subgroup_metrics_per_model_dct[model_name][
+            (all_subgroup_metrics_per_model_dct[model_name]['Subgroup'] == 'overall')
+            ]
+        subgroup_metrics_per_model_df['Subgroup'] = subgroup_metrics_per_model_df['Subgroup']
+        aligned_subgroup_metrics_per_model_df = subgroup_metrics_per_model_df[group_metrics_per_model_df.columns]
+
+        combined_metrics_per_model_df = pd.concat([group_metrics_per_model_df, aligned_subgroup_metrics_per_model_df]).reset_index(drop=True)
+        all_metrics_for_all_models_df = pd.concat([all_metrics_for_all_models_df, combined_metrics_per_model_df])
+
+    all_metrics_for_all_models_df = all_metrics_for_all_models_df.reset_index(drop=True)
+    all_metrics_for_all_models_df = all_metrics_for_all_models_df.drop(['Subgroup'], axis=1)
+
+    # Create new columns based on values in Metric and Value columns
+    pivoted_model_metrics_df = all_metrics_for_all_models_df.pivot(columns='Metric', values='Value',
+                                                                   index=[col for col in all_metrics_for_all_models_df.columns
+                                                                          if col not in ('Metric', 'Value')]).reset_index()
+    # Create a Model_Type column to count the number of models that satisfied the constraints based on their model types
+    pivoted_model_metrics_df['Model_Type'] = pivoted_model_metrics_df['Model_Name'].str.split('__', expand=True)[0]
+    model_types = pivoted_model_metrics_df['Model_Type'].unique()
+
+    # Create a pandas condition for filtering based on the input value ranges
+    models_in_range_df = pd.DataFrame()
+    df_with_models_satisfied_all_constraints = pd.DataFrame()
+    for idx, (metric_group, value_range) in enumerate(metrics_value_range_dct.items()):
+        pd_condition = None
+        if '&' not in metric_group:
+            min_range_val, max_range_val = value_range
+            if max_range_val < min_range_val:
+                raise ValueError('The second value in the input range must be greater than the first value, '
+                                 'so to be in the following format -- (min_range_val, max_range_val)')
+            metric = metric_group
+            pd_condition = (pivoted_model_metrics_df[metric] >= min_range_val) & (pivoted_model_metrics_df[metric] <= max_range_val)
+        else:
+            metrics = metric_group.split('&')
+            for idx, metric in enumerate(metrics):
+                min_range_val, max_range_val = metrics_value_range_dct[metric]
+                if max_range_val < min_range_val:
+                    raise ValueError('The second value in the input range must be greater than the first value, '
+                                     'so to be in the following format -- (min_range_val, max_range_val)')
+                if idx == 0:
+                    pd_condition = (pivoted_model_metrics_df[metric] >= min_range_val) & (pivoted_model_metrics_df[metric] <= max_range_val)
+                else:
+                    pd_condition &= (pivoted_model_metrics_df[metric] >= min_range_val) & (pivoted_model_metrics_df[metric] <= max_range_val)
+
+        num_satisfied_models_df = pivoted_model_metrics_df[pd_condition]['Model_Type'].value_counts().reset_index()
+        num_satisfied_models_df.rename(columns = {'Model_Type': 'Number_of_Models'}, inplace = True)
+        num_satisfied_models_df.rename(columns = {'index': 'Model_Type'}, inplace = True)
+        # If a constraint for a metric group is not satisfied, add zeros for all model names
+        if num_satisfied_models_df.shape[0] == 0:
+            num_satisfied_models_df = pd.DataFrame({'Model_Type': model_types,
+                                                    'Number_of_Models': [0] * len(model_types)})
+
+        num_satisfied_models_df['Metric_Group'] = metric_group
+        if idx == 0:
+            models_in_range_df = num_satisfied_models_df
+        else:
+            # Concatenate based on rows
+            models_in_range_df = pd.concat([models_in_range_df, num_satisfied_models_df], ignore_index=True, sort=False)
+
+        if metric_group.count('&') == 3:
+            df_with_models_satisfied_all_constraints = pivoted_model_metrics_df[pd_condition][['Model_Type', 'Model_Name']]
+
+    return models_in_range_df, df_with_models_satisfied_all_constraints
diff --git a/virny/utils/protected_groups_partitioning.py b/virny/utils/protected_groups_partitioning.py
index 658198d7..e13a20b6 100644
--- a/virny/utils/protected_groups_partitioning.py
+++ b/virny/utils/protected_groups_partitioning.py
@@ -70,8 +70,16 @@ def create_test_protected_groups(X_test: pd.DataFrame, init_features_df: pd.Data
 
     """
     plain_sensitive_attributes = [attr for attr in sensitive_attributes_dct.keys() if INTERSECTION_SIGN not in attr]
-    X_test_with_sensitive_attrs = init_features_df[plain_sensitive_attributes].loc[X_test.index]
 
+    # Check spelling of sensitive attributes
+    attrs_with_errors = []
+    for attr in plain_sensitive_attributes:
+        if attr not in init_features_df.columns:
+            attrs_with_errors.append(attr)
+    if len(attrs_with_errors) > 0:
+        raise ValueError(f"At least one of sensitive attributes is not in dataset columns. Check spelling of {attrs_with_errors} attributes.")
+
+    X_test_with_sensitive_attrs = init_features_df[plain_sensitive_attributes].loc[X_test.index]
     groups = dict()
     for attr in sensitive_attributes_dct.keys():
         attr = attr.strip()