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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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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)
Please rerun this cell to show the HTML repr or trust the notebook.

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

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Model Card Contact

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Citation

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BibTeX:

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