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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Hyperparameter	Value
aggressive_elimination	False
cv	5
error_score	nan
estimator__categorical_features
estimator__early_stopping	auto
estimator__l2_regularization	0.0
estimator__learning_rate	0.1
estimator__loss	auto
estimator__max_bins	255
estimator__max_depth
estimator__max_iter	100
estimator__max_leaf_nodes	31
estimator__min_samples_leaf	20
estimator__monotonic_cst
estimator__n_iter_no_change	10
estimator__random_state
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
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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Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value

How to Get Started with the Model

Use the code below to get started with the model.

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Model Card Authors

This model card is written by following authors:

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Model Card Contact

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Citation

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BibTeX:

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