5_Improving_a_model_HyperparameterTuning.ipynb

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    "colab": {
      "name": "5 . Improving a model-Hyperparameter.ipynb",
      "provenance": [],
      "mount_file_id": "1mcuVN8sW5lpF_ecjhq_Dov_mWY-Xtwpw",
      "authorship_tag": "ABX9TyNxbuLoYdMM2M5cCnHe4Z3i",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aakashkumarme/ML-LEARN/blob/main/5_Improving_a_model_HyperparameterTuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1I4N_i1o2fyv",
        "outputId": "6822e2ed-47a4-4a61-e590-dd0e93736ce6"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier()\n",
        "clf.get_params()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': True,\n",
              " 'ccp_alpha': 0.0,\n",
              " 'class_weight': None,\n",
              " 'criterion': 'gini',\n",
              " 'max_depth': None,\n",
              " 'max_features': 'auto',\n",
              " 'max_leaf_nodes': None,\n",
              " 'max_samples': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_impurity_split': None,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'n_estimators': 100,\n",
              " 'n_jobs': None,\n",
              " 'oob_score': False,\n",
              " 'random_state': None,\n",
              " 'verbose': 0,\n",
              " 'warm_start': False}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOvttgCj4zo3"
      },
      "source": [
        " Three ways to adjust hyperparameter\n",
        "\n",
        "\n",
        "1.   By hand\n",
        "2.   Randomly with RandomSearchCV\n",
        "3.   Randomly with GridSearchCV\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FRY1z2v0w6kV"
      },
      "source": [
        "# **# 1. By Hand :**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_Gl1lmV3-R1"
      },
      "source": [
        "# 1. By Hand :\n",
        "\n",
        "\n",
        "def evaluate_preds(y_true,y_preds):\n",
        "    accuracy = accuracy_score(y_true,y_preds)\n",
        "    precision = precision_score(y_true,y_preds)\n",
        "    recall = recall_score(y_true,y_preds)\n",
        "    f1 = f1_score(y_true,y_preds)\n",
        "    metric_dict = {\"accuracy \" : round(accuracy,2),\n",
        "                  \"precision \" : round(precision,2),\n",
        "                  \"recall \" : round(recall,2),\n",
        "                  \"f1 \" : round(f1,2)}\n",
        "\n",
        "    print(f\"Acc: {accuracy * 100:.2f}%\" )\n",
        "    print(f\"precision:{precision:.2f}%\" )\n",
        "    print(f\"recall:{recall:.2f}%\" )\n",
        "    print(f\"f1: {f1:.2f}%\" )\n",
        "    return metric_dict"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fOKCmgwGoONw",
        "outputId": "c979e5ee-2e5c-4004-fe0b-6999c9a24a87"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "np.random.seed(42)\n",
        "heart_diseases = pd.read_csv(\"/content/drive/MyDrive/dataset/heart-disease.csv\")\n",
        "heart_diseases.head()\n",
        "\n",
        "#shuffle the data\n",
        "heart_diseases_shuffled = heart_diseases.sample(frac=1)\n",
        "\n",
        "X = heart_diseases_shuffled.drop(\"target\",axis = 1)\n",
        "y = heart_diseases_shuffled[\"target\"]\n",
        "\n",
        "#Split the data into train, validation and test\n",
        "train_split = round(0.7 * len(heart_diseases_shuffled))\n",
        "valid_split = round(train_split + 0.15 * len(heart_diseases_shuffled))\n",
        "\n",
        "X_train,y_train = X[:train_split],y[:train_split]\n",
        "X_valid,y_valid = X[train_split:valid_split],y[train_split:valid_split]\n",
        "X_test,y_test = X[valid_split:],y[:valid_split]\n",
        "\n",
        "len(X_train) , len(X_valid) , len(X_test)\n",
        "\n",
        "clf = RandomForestClassifier()\n",
        "clf.fit(X_train,y_train)\n",
        "y_preds = clf.predict(X_valid)\n",
        "\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score #import the parameters\n",
        "#baseline prediction\n",
        "baseline_metrics = evaluate_preds(y_valid,y_preds)\n",
        "baseline_metrics"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Acc: 82.22%\n",
            "precision:0.81%\n",
            "recall:0.88%\n",
            "f1: 0.85%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy ': 0.82, 'f1 ': 0.85, 'precision ': 0.81, 'recall ': 0.88}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "76h5ESZoo_ox",
        "outputId": "7cfd8d65-28f1-4cd3-d934-d0b7249258bd"
      },
      "source": [
        " #adjust hyperparamter-1\n",
        " # Create a second classifier with different hyper parameter\n",
        "\n",
        "np.random.seed(42)\n",
        "clf2 = RandomForestClassifier(n_estimators=10)\n",
        "clf2.fit(X_train,y_train)\n",
        "y_preds2 = clf2.predict(X_valid)\n",
        "\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score #import the parameters\n",
        "#baseline prediction\n",
        "clf2_metrics = evaluate_preds(y_valid,y_preds2)\n",
        "clf2_metrics"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Acc: 82.22%\n",
            "precision:0.84%\n",
            "recall:0.84%\n",
            "f1: 0.84%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy ': 0.82, 'f1 ': 0.84, 'precision ': 0.84, 'recall ': 0.84}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70ANi1fovzmw",
        "outputId": "17202486-c066-444f-b1de-42239fb63f88"
      },
      "source": [
        " #adjust hyperparamter-2\n",
        " # Create a second classifier with different hyper parameter\n",
        "\n",
        "np.random.seed(42)\n",
        "clf3 = RandomForestClassifier(n_estimators=100,max_depth=10)\n",
        "clf3.fit(X_train,y_train)\n",
        "y_preds2 = clf3.predict(X_valid)\n",
        "\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score #import the parameters\n",
        "#baseline prediction\n",
        "clf3_metrics = evaluate_preds(y_valid,y_preds2)\n",
        "clf3_metrics"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Acc: 80.00%\n",
            "precision:0.81%\n",
            "recall:0.84%\n",
            "f1: 0.82%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy ': 0.8, 'f1 ': 0.82, 'precision ': 0.81, 'recall ': 0.84}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGruir53xD16"
      },
      "source": [
        "# **# 2. By RandomSearchCV :**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDSYieefwtNv",
        "outputId": "66c528e3-064c-4c67-c658-ae628251c8c1"
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "grid = {\"n_estimators\": [10,100,200,500,1000,1200],\n",
        "        \"max_depth\" : [None,5,10,20,30],\n",
        "        \"max_features\": [\"auto\",\"sqrt\"],\n",
        "        \"min_samples_split\": [2,4,6],\n",
        "        \"min_samples_leaf\":[1,2,4]}\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "X = heart_diseases_shuffled.drop(\"target\",axis = 1)\n",
        "y = heart_diseases_shuffled[\"target\"]\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_jobs=1)\n",
        "\n",
        "#Setup RandomizesSearchCV\n",
        "rs_clf = RandomizedSearchCV(estimator = clf ,\n",
        "                            param_distributions = grid,\n",
        "                            n_iter=10,\n",
        "                            cv = 5,\n",
        "                            verbose = 2) \n",
        "#Fit  RandomizedSearchCV version of clf\n",
        "rs_clf.fit(X_train,y_train)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
            "[CV] n_estimators=1200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  n_estimators=1200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.9s\n",
            "[CV] n_estimators=1200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.9s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  n_estimators=1200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   2.0s\n",
            "[CV] n_estimators=1200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
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            "[CV] n_estimators=1200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
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            "[CV] n_estimators=1200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
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            "[CV] n_estimators=100, min_samples_split=4, min_samples_leaf=2, max_features=auto, max_depth=30 \n",
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            "[CV] n_estimators=100, min_samples_split=6, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
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            "[CV] n_estimators=100, min_samples_split=6, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
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            "[CV] n_estimators=100, min_samples_split=6, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
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            "[CV] n_estimators=10, min_samples_split=4, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
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            "[CV] n_estimators=10, min_samples_split=4, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
            "[CV]  n_estimators=10, min_samples_split=4, min_samples_leaf=2, max_features=auto, max_depth=10, total=   0.0s\n",
            "[CV] n_estimators=500, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
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            "[CV] n_estimators=500, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
            "[CV]  n_estimators=500, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=   0.8s\n",
            "[CV] n_estimators=500, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
            "[CV]  n_estimators=500, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=   0.8s\n",
            "[CV] n_estimators=500, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
            "[CV]  n_estimators=500, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=   0.8s\n",
            "[CV] n_estimators=500, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
            "[CV]  n_estimators=500, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=   0.8s\n",
            "[CV] n_estimators=200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
            "[CV]  n_estimators=200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=   0.4s\n",
            "[CV] n_estimators=200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
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            "[CV] n_estimators=200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
            "[CV]  n_estimators=200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=   0.3s\n",
            "[CV] n_estimators=200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
            "[CV]  n_estimators=200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=   0.3s\n",
            "[CV] n_estimators=200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
            "[CV]  n_estimators=200, min_samples_split=6, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=   0.3s\n",
            "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10 \n",
            "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10, total=   0.3s\n",
            "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10 \n",
            "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10, total=   0.3s\n",
            "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10 \n",
            "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10, total=   0.3s\n",
            "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10 \n",
            "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10, total=   0.3s\n",
            "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10 \n",
            "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=auto, max_depth=10, total=   0.3s\n",
            "[CV] n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20 \n",
            "[CV]  n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20, total=   1.6s\n",
            "[CV] n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20 \n",
            "[CV]  n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20, total=   1.6s\n",
            "[CV] n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20 \n",
            "[CV]  n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20, total=   1.6s\n",
            "[CV] n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20 \n",
            "[CV]  n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20, total=   1.5s\n",
            "[CV] n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20 \n",
            "[CV]  n_estimators=1000, min_samples_split=4, min_samples_leaf=2, max_features=sqrt, max_depth=20, total=   1.6s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   28.4s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score=nan,\n",
              "                   estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                    ccp_alpha=0.0,\n",
              "                                                    class_weight=None,\n",
              "                                                    criterion='gini',\n",
              "                                                    max_depth=None,\n",
              "                                                    max_features='auto',\n",
              "                                                    max_leaf_nodes=None,\n",
              "                                                    max_samples=None,\n",
              "                                                    min_impurity_decrease=0.0,\n",
              "                                                    min_impurity_split=None,\n",
              "                                                    min_samples_leaf=1,\n",
              "                                                    min_samples_split=2,\n",
              "                                                    min_weight_fraction_leaf=0.0,\n",
              "                                                    n_estimators=100, n_jobs...\n",
              "                                                    random_state=None,\n",
              "                                                    verbose=0,\n",
              "                                                    warm_start=False),\n",
              "                   iid='deprecated', n_iter=10, n_jobs=None,\n",
              "                   param_distributions={'max_depth': [None, 5, 10, 20, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 4],\n",
              "                                        'min_samples_split': [2, 4, 6],\n",
              "                                        'n_estimators': [10, 100, 200, 500,\n",
              "                                                         1000, 1200]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nAJlKFTOzNJ5",
        "outputId": "59b3d4d1-1aef-49e1-90c9-28386aa72043"
      },
      "source": [
        "  rs_clf.best_params_"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': None,\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 2,\n",
              " 'min_samples_split': 6,\n",
              " 'n_estimators': 200}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqJeYuBv1R99",
        "outputId": "2fd3949f-0021-4de6-a787-ffb13eb50fbe"
      },
      "source": [
        "rs_y_preds = rs_clf.predict(X_test)\n",
        "\n",
        "rs_metrics = evaluate_preds(y_test,rs_y_preds)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Acc: 81.97%\n",
            "precision:0.77%\n",
            "recall:0.86%\n",
            "f1: 0.81%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4hvE9Sl1zif"
      },
      "source": [
        "# **# 3. By GridSearchCV :**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EsonjEdz1no_",
        "outputId": "0b27dce5-e44c-4126-dd72-038592f7c38d"
      },
      "source": [
        "grid"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': [None, 5, 10, 20, 30],\n",
              " 'max_features': ['auto', 'sqrt'],\n",
              " 'min_samples_leaf': [1, 2, 4],\n",
              " 'min_samples_split': [2, 4, 6],\n",
              " 'n_estimators': [10, 100, 200, 500, 1000, 1200]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TsX1_5g2Mxk"
      },
      "source": [
        "grid2 = {'max_depth': [None],\n",
        "         'max_features': ['auto', 'sqrt'],\n",
        "         'min_samples_leaf': [1, 2],\n",
        "         'min_samples_split': [6],\n",
        "         'n_estimators': [100, 200, 500]}"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqxfP7CC3Boc",
        "outputId": "07baac39-4136-40ee-a144-52ff1a9fcddd"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "np.random.seed(42)\n",
        "\n",
        "X = heart_diseases_shuffled.drop(\"target\",axis = 1)\n",
        "y = heart_diseases_shuffled[\"target\"]\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_jobs=1)\n",
        "\n",
        "#Setup GridSearchCV\n",
        "gs_clf = GridSearchCV(estimator = clf ,\n",
        "                            param_grid = grid2,\n",
        "                            cv = 5,\n",
        "                            verbose = 2) \n",
        "#Fit  GridSearchCV version of clf\n",
        "gs_clf.fit(X_train,y_train)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.7s\n",
            "[CV] max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.7s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100, total=   0.2s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200, total=   0.3s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.8s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.9s\n",
            "[CV] max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500 \n",
            "[CV]  max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500, total=   0.8s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  60 out of  60 | elapsed:   25.3s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                              class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=100, n_jobs=1,\n",
              "                                              oob_score=False,\n",
              "                                              random_state=None, verbose=0,\n",
              "                                              warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'max_depth': [None], 'max_features': ['auto', 'sqrt'],\n",
              "                         'min_samples_leaf': [1, 2], 'min_samples_split': [6],\n",
              "                         'n_estimators': [100, 200, 500]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlSVBz9t3fsW",
        "outputId": "5e450c39-b220-4e49-9ed5-ac5492331de9"
      },
      "source": [
        "gs_clf.best_params_"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': None,\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 6,\n",
              " 'n_estimators': 200}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wNczEkX3-FK",
        "outputId": "4a50b9c9-9cd2-45a5-b772-1d87ff2ef3ef"
      },
      "source": [
        "gs_y_preds = gs_clf.predict(X_test)\n",
        "\n",
        "gs_metrics = evaluate_preds(y_test,gs_y_preds)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Acc: 78.69%\n",
            "precision:0.74%\n",
            "recall:0.82%\n",
            "f1: 0.78%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQBJy2_54JAW"
      },
      "source": [
        "Comparing different models metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "vw_wmZH54VLx",
        "outputId": "f49b9c9c-965d-4bcb-9003-d8491a7fc9f2"
      },
      "source": [
        "\n",
        "compare_metrics = pd.DataFrame({\"baseline\":baseline_metrics,\n",
        "                                \"clf_2\": clf2_metrics,\n",
        "                                \"random search\" : rs_metrics,\n",
        "                                \"grid search\" : gs_metrics})\n",
        "                               \n",
        "compare_metrics.plot.bar(figsize=(10, 8));"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoBPfKLx4tBe"
      },
      "source": [
        " "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}