osanseviero/hf_hub_example-f7d1d7e5-f207-4eef-99bb-57408d604e2b

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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