--- library_name: sklearn tags: - sklearn - skops - tabular-classification widget: structuredData: area error: - 30.29 - 96.05 - 48.31 compactness error: - 0.01911 - 0.01652 - 0.01484 concave points error: - 0.01037 - 0.0137 - 0.01093 concavity error: - 0.02701 - 0.02269 - 0.02813 fractal dimension error: - 0.003586 - 0.001698 - 0.002461 mean area: - 481.9 - 1130.0 - 748.9 mean compactness: - 0.1058 - 0.1029 - 0.1223 mean concave points: - 0.03821 - 0.07951 - 0.08087 mean concavity: - 0.08005 - 0.108 - 0.1466 mean fractal dimension: - 0.06373 - 0.05461 - 0.05796 mean perimeter: - 81.09 - 123.6 - 101.7 mean radius: - 12.47 - 18.94 - 15.46 mean smoothness: - 0.09965 - 0.09009 - 0.1092 mean symmetry: - 0.1925 - 0.1582 - 0.1931 mean texture: - 18.6 - 21.31 - 19.48 perimeter error: - 2.497 - 5.486 - 3.094 radius error: - 0.3961 - 0.7888 - 0.4743 smoothness error: - 0.006953 - 0.004444 - 0.00624 symmetry error: - 0.01782 - 0.01386 - 0.01397 texture error: - 1.044 - 0.7975 - 0.7859 worst area: - 677.9 - 1866.0 - 1156.0 worst compactness: - 0.2378 - 0.2336 - 0.2394 worst concave points: - 0.1015 - 0.1789 - 0.1514 worst concavity: - 0.2671 - 0.2687 - 0.3791 worst fractal dimension: - 0.0875 - 0.06589 - 0.08019 worst perimeter: - 96.05 - 165.9 - 124.9 worst radius: - 14.97 - 24.86 - 19.26 worst smoothness: - 0.1426 - 0.1193 - 0.1546 worst symmetry: - 0.3014 - 0.2551 - 0.2837 worst texture: - 24.64 - 26.58 - 26.0 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.

Click to expand

| 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.

Click to expand

```python [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] ```