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🚑 Fix compatibility issue
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@EssamWisam
EssamWisam committed Oct 4, 2023
1 parent 0ae67c7 commit 6ef3526
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55 changes: 32 additions & 23 deletions example/BalancedBagging.ipynb
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Expand Up @@ -40,16 +40,16 @@
{
"data": {
"text/plain": [
"((Column1 = [0.9695150609084499, 0.012898301755861596, 0.7555027304121053, 0.3467415729179013, 0.35969402837473463, 0.2601876747805505, 0.9522580699968279, 0.06304475092339623, 0.18909001622655808, 0.19934942931986965 … 0.021532597906190776, 0.8482825697641306, 0.10773487816863903, 0.32189982199036116, 0.12662208474317038, 0.28529465447429614, 0.2907506630258835, 0.36872799387588473, 0.061489791166806085, 0.45645058368583713], Column2 = [0.06546916714160167, 0.7243956502957003, 0.5183099801474415, 0.7555562860508294, 0.11226218114407538, 0.9135150277876691, 0.8739421974558176, 0.2268482788660101, 0.580604436651146, 0.4142252330250549 … 0.6517425913240111, 0.01713263102740481, 0.7175499403837856, 0.7362894157420817, 0.24893665902538054, 0.41499951381631595, 0.2159527717429719, 0.8966879835264249, 0.87252430655793, 0.41461921031276117], Column3 = [0.5939320702328891, 0.19329886972497456, 0.04656947038518311, 0.22095698685781184, 0.678807659662497, 0.12720198818430306, 0.6795750371448686, 0.9314917999820301, 0.22920734893984274, 0.5148148980955375 … 0.55049773593343, 0.038576459283091946, 0.27765727942909757, 0.2753072414696357, 0.8823620780359746, 0.44831794170895023, 0.9073846432163745, 0.4648550947905655, 0.311984726769037, 0.25829997798611304], Column4 = [0.12253944650540982, 0.8259140842535423, 0.4034477332184384, 0.5279399406265695, 0.5579944087437719, 0.24650366028608328, 0.6874897000162434, 0.23391406844015605, 0.5641254897013973, 0.6250622796341656 … 0.21708181942178983, 0.35224683896541464, 0.8444113778983325, 0.4547214584884428, 0.13508852017592232, 0.9510137735662383, 0.5723463533029658, 0.626377972762265, 0.7854013810594317, 0.15394691114473347], Column5 = [0.47958743625921163, 0.45779753417165514, 0.6367059235247621, 0.8601116026079643, 0.3334020182022719, 0.41593698717526373, 0.13208968772625174, 0.16951044109747648, 0.8137887839507706, 0.4429229861115882 … 0.01308976221980429, 0.48597926808091163, 0.20768781798463476, 0.30045611276046247, 0.15759293576302558, 0.975806377881983, 0.19451065500145392, 0.9638103356367584, 0.3594043445295293, 0.7792867217495332], Column6 = [3.0, 3.0, 1.0, 3.0, 1.0, 2.0, 3.0, 2.0, 3.0, 3.0 … 3.0, 2.0, 1.0, 2.0, 1.0, 2.0, 2.0, 3.0, 3.0, 1.0], Column7 = [2.0, 2.0, 2.0, 2.0, 1.0, 2.0, 2.0, 2.0, 1.0, 1.0 … 2.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0]), CategoricalArrays.CategoricalValue{Int64, UInt32}[0, 0, 0, 0, 0, 0, 0, 0, 1, 0 … 0, 0, 1, 0, 1, 0, 0, 0, 0, 0])"
"((Column1 = [0.564, 0.862, 0.793, 0.505, 0.683, 0.699, 0.545, 0.693, 0.95, 0.44 … 0.423, 0.632, 0.922, 0.592, 0.944, 0.517, 0.785, 0.579, 0.725, 0.711], Column2 = [0.42, 0.715, 0.358, -0.009, 0.228, 0.725, 0.786, 0.52, 0.646, 0.582 … 0.65, 0.633, 0.263, 0.141, 0.472, 0.45, -0.019, 0.593, 0.777, 0.877], Column3 = [0.638, 0.719, 0.716, 0.604, 0.616, 0.784, 0.697, 0.711, 0.878, 0.739 … 0.722, 0.672, 0.879, 0.598, 0.879, 0.669, 0.728, 0.768, 0.736, 0.725], Column4 = [0.29, 0.164, 0.164, 0.262, 0.246, 0.211, 0.155, 0.03, 1.842, 0.324 … 0.192, 0.143, 1.323, 0.251, 1.084, 0.165, 0.138, 0.176, 0.155, 0.217], Column5 = [0.605, 0.287, 0.565, 0.121, 0.752, 0.317, 0.165, 0.497, 0.361, 0.293 … 0.726, 0.781, 0.694, 0.728, 0.692, 0.351, 0.089, 0.478, 0.067, -0.19], Column6 = [2.0, 1.0, 3.0, 1.0, 3.0, 1.0, 3.0, 2.0, 2.0, 3.0 … 1.0, 3.0, 2.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0], Column7 = [2.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0, 2.0, 1.0, 2.0 … 1.0, 2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0]), CategoricalArrays.CategoricalValue{Int64, UInt32}[0, 0, 0, 0, 0, 0, 0, 0, 1, 0 … 0, 0, 1, 0, 1, 0, 0, 0, 0, 0])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X, y = generate_imbalanced_data(100, 5; cat_feats_num_vals = [3, 2], \n",
" probs = [0.9, 0.1], \n",
"X, y = generate_imbalanced_data(100, 5; num_vals_per_category = [3, 2], \n",
" class_probs = [0.9, 0.1], \n",
" type = \"ColTable\", \n",
" rng=42)"
]
Expand All @@ -73,6 +73,15 @@
"WARNING: using StaticArrays.setindex in module FiniteDiff conflicts with an existing identifier.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Warning: The call to compilecache failed to create a usable precompiled cache file for MLJLinearModels [6ee0df7b-362f-4a72-a706-9e79364fb692]\n",
"│ exception = ErrorException(\"Required dependency Optim [429524aa-4258-5aef-a3af-852621145aeb] failed to load from a cache file.\")\n",
"└ @ Base loading.jl:1349\n"
]
},
{
"data": {
"text/plain": [
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},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"data": {
"text/plain": [
"100-element CategoricalDistributions.UnivariateFiniteVector{Multiclass{2}, Int64, UInt32, Float64}:\n",
" UnivariateFinite{Multiclass{2}}(0=>0.928, 1=>0.0722)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.845, 1=>0.155)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.749, 1=>0.251)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.902, 1=>0.0977)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.804, 1=>0.196)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.864, 1=>0.136)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.851, 1=>0.149)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.954, 1=>0.0458)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.853, 1=>0.147)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.86, 1=>0.14)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.0, 1=>1.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" ⋮\n",
" UnivariateFinite{Multiclass{2}}(0=>0.671, 1=>0.329)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.73, 1=>0.27)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.843, 1=>0.157)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.941, 1=>0.0594)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.872, 1=>0.128)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.92, 1=>0.0797)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.929, 1=>0.0714)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.791, 1=>0.209)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.827, 1=>0.173)"
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.0, 1=>1.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>0.0, 1=>1.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)\n",
" UnivariateFinite{Multiclass{2}}(0=>1.0, 1=>0.0)"
]
},
"metadata": {},
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238 changes: 177 additions & 61 deletions example/BalancedModel.ipynb
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"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `~/Documents/GitHub/MLJBalancing/example`\n"
]
}
],
"source": [
"ENV[\"JULIA_PKG_SERVER\"] = \"\"\n",
"using Pkg\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"0: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 204 (40.9%) \n",
"1: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 297 (59.5%) \n",
"2: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 499 (100.0%) \n"
"0: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 189 (37.4%) \n",
"1: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 305 (60.3%) \n",
"2: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 506 (100.0%) \n"
]
}
],
"source": [
"X, y = Imbalance.generate_imbalanced_data(1000, 5; probs=[0.2, 0.3, 0.5])\n",
"X, y = Imbalance.generate_imbalanced_data(1000, 5; class_probs=[0.2, 0.3, 0.5])\n",
"X = DataFrame(X)\n",
"(X_train, X_test), (y_train, y_test) = partition((X, y), 0.8, rng=123, multi=true)\n",
"Imbalance.checkbalance(y)"
Expand Down Expand Up @@ -144,6 +152,7 @@
" balancer2 = SMOTENC(\n",
" k = 10, \n",
" ratios = 1.2, \n",
" knn_tree = \"Brute\", \n",
" rng = 42, \n",
" try_perserve_type = true), \n",
" balancer3 = ROSE(\n",
Expand Down Expand Up @@ -173,43 +182,182 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Training machine(BalancedModelProbabilistic(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).\n",
"└ @ MLJBase /Users/essam/.julia/packages/MLJBase/ByFwA/src/machines.jl:492\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Training machine(ROSE(s = 1.0, …), …).\n",
"└ @ MLJBase /Users/essam/.julia/packages/MLJBase/ByFwA/src/machines.jl:492\n",
"┌ Info: Training machine(SMOTENC(k = 10, …), …).\n",
"└ @ MLJBase /Users/essam/.julia/packages/MLJBase/ByFwA/src/machines.jl:492\n",
"┌ Info: Training machine(RandomOversampler(ratios = 1.0, …), …).\n",
"└ @ MLJBase /Users/essam/.julia/packages/MLJBase/ByFwA/src/machines.jl:492\n",
"┌ Info: Training machine(:model, …).\n",
"└ @ MLJBase /Users/essam/.julia/packages/MLJBase/ByFwA/src/machines.jl:492\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r\u001b[32mProgress: 67%|███████████████████████████▍ | ETA: 0:00:00\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 0\u001b[39m\u001b[K\r\u001b[A"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r\u001b[32mProgress: 67%|███████████████████████████▍ | ETA: 0:00:01\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 1\u001b[39m\u001b[K\r\u001b[A"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\r\u001b[K\u001b[A\r\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:01\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 0\u001b[39m\u001b[K\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r\u001b[32mProgress: 67%|███████████████████████████▍ | ETA: 0:00:00\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 1\u001b[39m\u001b[K\r\u001b[A"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\r\u001b[K\u001b[A\r\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:00\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 0\u001b[39m\u001b[K\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r\u001b[32mProgress: 67%|███████████████████████████▍ | ETA: 0:00:00\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 1\u001b[39m\u001b[K\r\u001b[A"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\r\u001b[K\u001b[A\r\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:00\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 0\u001b[39m\u001b[K\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r\u001b[32mProgress: 67%|███████████████████████████▍ | ETA: 0:00:00\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 1\u001b[39m\u001b[K\r\u001b[A"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\r\u001b[K\u001b[A\r\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:00\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 0\u001b[39m\u001b[K\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r\u001b[32mProgress: 67%|███████████████████████████▍ | ETA: 0:00:00\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 1\u001b[39m\u001b[K\r\u001b[A"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\r\u001b[K\u001b[A\r\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:00\u001b[39m\u001b[K\r\n",
"\u001b[34m class: 0\u001b[39m\u001b[K\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, NamedTuple{(), Tuple{}}}\n",
"│ optim_options: Optim.Options{Float64, Nothing}\n",
"│ lbfgs_options: NamedTuple{(), Tuple{}} NamedTuple()\n",
"└ @ MLJLinearModels /Users/essam/.julia/packages/MLJLinearModels/zSQnL/src/mlj/interface.jl:72\n"
]
},
{
"data": {
"text/plain": [
"trained Machine; does not cache data\n",
" model: BalancedModelProbabilistic(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …)\n",
" args: \n",
" 1:\tSource @226 ⏎ Table{AbstractVector{Continuous}}\n",
" 2:\tSource @078 ⏎ AbstractVector{Multiclass{3}}\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mach = machine(balanced_model, X_train, y_train)\n",
"fit!(mach)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"200-element CategoricalDistributions.UnivariateFiniteVector{Multiclass{3}, Int64, UInt32, Float64}:\n",
" UnivariateFinite{Multiclass{3}}(0=>0.359, 1=>0.295, 2=>0.346)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.384, 1=>0.294, 2=>0.322)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.301, 1=>0.395, 2=>0.304)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.285, 1=>0.369, 2=>0.346)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.279, 1=>0.39, 2=>0.331)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.31, 1=>0.34, 2=>0.35)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.292, 1=>0.392, 2=>0.316)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.331, 1=>0.351, 2=>0.318)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.303, 1=>0.35, 2=>0.347)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.311, 1=>0.351, 2=>0.338)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>1.0, 2=>4.16e-270)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>1.2e-217, 2=>1.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>2.99e-304, 1=>1.0, 2=>1.19e-221)\n",
" UnivariateFinite{Multiclass{3}}(0=>1.0, 1=>1.35e-179, 2=>2.0900000000000003e-267)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>1.36e-93, 2=>1.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>4.01e-71, 2=>1.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>1.16e-270, 1=>4.55e-103, 2=>1.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>1.0, 1=>1.0299999999999999e-198, 2=>0.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>1.0, 1=>2.2100000000000002e-73, 2=>1.45e-97)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>3.4900000000000003e-75, 2=>1.0)\n",
" ⋮\n",
" UnivariateFinite{Multiclass{3}}(0=>0.319, 1=>0.354, 2=>0.326)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.375, 1=>0.291, 2=>0.334)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.345, 1=>0.329, 2=>0.326)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.312, 1=>0.343, 2=>0.345)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.358, 1=>0.308, 2=>0.333)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.307, 1=>0.344, 2=>0.349)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.297, 1=>0.36, 2=>0.343)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.358, 1=>0.312, 2=>0.33)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.355, 1=>0.309, 2=>0.336)"
" UnivariateFinite{Multiclass{3}}(0=>1.3699999999999999e-239, 1=>9.34e-140, 2=>1.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>1.0, 2=>2.3599999999999997e-256)\n",
" UnivariateFinite{Multiclass{3}}(0=>3.03e-149, 1=>1.69e-109, 2=>1.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>1.0, 2=>3.3999999999999996e-242)\n",
" UnivariateFinite{Multiclass{3}}(0=>8.889999999999998e-259, 1=>8.98e-152, 2=>1.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>1.5500000000000002e-235, 1=>7.45e-95, 2=>1.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>1.0, 2=>4.3e-232)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>2.31e-134, 2=>1.0)\n",
" UnivariateFinite{Multiclass{3}}(0=>0.0, 1=>1.0, 2=>1.21e-194)"
]
},
"metadata": {},
Expand Down Expand Up @@ -250,41 +398,9 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BalancedModelProbabilistic(\n",
" model = LogisticClassifier(\n",
" lambda = 2.220446049250313e-16, \n",
" gamma = 0.0, \n",
" penalty = :l2, \n",
" fit_intercept = true, \n",
" penalize_intercept = false, \n",
" scale_penalty_with_samples = true, \n",
" solver = nothing), \n",
" balancer1 = RandomOversampler(\n",
" ratios = 1.4, \n",
" rng = 42, \n",
" try_perserve_type = true), \n",
" balancer2 = SMOTENC(\n",
" k = 10, \n",
" ratios = 1.2, \n",
" rng = 42, \n",
" try_perserve_type = true), \n",
" balancer3 = ROSE(\n",
" s = 0.0, \n",
" ratios = 1.3, \n",
" rng = 42, \n",
" try_perserve_type = true))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"fitted_params(mach).best_model"
]
Expand Down
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