YAML Metadata
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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.
HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)
HistGradientBoostingClassifier()
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
You can contact the model card authors through following channels: [More Information Needed]
Citation
Below you can find information related to citation.
BibTeX:
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