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Model description

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Intended uses & limitations

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Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

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Hyperparameters Value
aggressive_elimination False
cv 5
error_score nan
estimator__categorical_features None
estimator__early_stopping auto
estimator__l2_regularization 0.0
estimator__learning_rate 0.1
estimator__loss log_loss
estimator__max_bins 255
estimator__max_depth None
estimator__max_iter 100
estimator__max_leaf_nodes 31
estimator__min_samples_leaf 20
estimator__monotonic_cst None
estimator__n_iter_no_change 10
estimator__random_state None
estimator__scoring loss
estimator__tol 1e-07
estimator__validation_fraction 0.1
estimator__verbose 0
estimator__warm_start False
estimator HistGradientBoostingClassifier()
factor 3
max_resources auto
min_resources exhaust
n_jobs -1
param_grid {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]}
random_state 42
refit True
resource n_samples
return_train_score True
scoring None
verbose 0

Model Plot

The model plot is below.

HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)
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How to Get Started with the Model

Use the code below to get started with the model.

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Model Card Authors

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Citation

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