--- library_name: sklearn --- # Model description This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max_leaf_nodes and max_depth. ## Intended uses & limitations This model is not ready to be used in production. ## 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)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# How to Get Started with the Model Use the code below to get started with the model.

Click to expand

``` import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) ```

# Model Card Authors This model card is written by following authors: skops_user # 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] ``` confusion_matrix ![confusion_matrix](confusion_matrix.png)