--- library_name: sklearn --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.
Click to expand | 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.
Click to expand ``` [More Information Needed] ```
# Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ```