CatBoostClassifier

class CatBoostClassifier(iterations=None,
                         learning_rate=None,
                         depth=None,
                         l2_leaf_reg=None,
                         model_size_reg=None,
                         rsm=None,
                         loss_function=None,
                         border_count=None,
                         feature_border_type=None,
                         per_float_feature_quantization=None,
                         input_borders=None,
                         output_borders=None,
                         fold_permutation_block=None,
                         od_pval=None,
                         od_wait=None,
                         od_type=None,
                         nan_mode=None,
                         counter_calc_method=None,
                         leaf_estimation_iterations=None,
                         leaf_estimation_method=None,
                         thread_count=None,
                         random_seed=None,
                         use_best_model=None,
                         verbose=None,
                         logging_level=None,
                         metric_period=None,
                         ctr_leaf_count_limit=None,
                         store_all_simple_ctr=None,
                         max_ctr_complexity=None,
                         has_time=None,
                         allow_const_label=None,
                         classes_count=None,
                         class_weights=None,
                         auto_class_weights=None,
                         one_hot_max_size=None,
                         random_strength=None,
                         name=None,
                         ignored_features=None,
                         train_dir=None,
                         custom_loss=None,
                         custom_metric=None,
                         eval_metric=None,
                         bagging_temperature=None,
                         save_snapshot=None,
                         snapshot_file=None,
                         snapshot_interval=None,
                         fold_len_multiplier=None,
                         used_ram_limit=None,
                         gpu_ram_part=None,
                         allow_writing_files=None,
                         final_ctr_computation_mode=None,
                         approx_on_full_history=None,
                         boosting_type=None,
                         simple_ctr=None,
                         combinations_ctr=None,
                         per_feature_ctr=None,
                         task_type=None,
                         device_config=None,
                         devices=None,
                         bootstrap_type=None,
                         subsample=None,
                         sampling_unit=None,
                         dev_score_calc_obj_block_size=None,
                         max_depth=None,
                         n_estimators=None,
                         num_boost_round=None,
                         num_trees=None,
                         colsample_bylevel=None,
                         random_state=None,
                         reg_lambda=None,
                         objective=None,
                         eta=None,
                         max_bin=None,
                         scale_pos_weight=None,
                         gpu_cat_features_storage=None,
                         data_partition=None
                         metadata=None,
                         early_stopping_rounds=None,
                         cat_features=None,
                         grow_policy=None,
                         min_data_in_leaf=None,
                         min_child_samples=None,
                         max_leaves=None,
                         num_leaves=None,
                         score_function=None,
                         leaf_estimation_backtracking=None,
                         ctr_history_unit=None,
                         monotone_constraints=None,
                         feature_weights=None,
                         penalties_coefficient=None,
                         first_feature_use_penalties=None,
                         model_shrink_rate=None,
                         model_shrink_mode=None,
                         langevin=None,
                         diffusion_temperature=None,
                         posterior_sampling=None,
                         boost_from_average=None,
                         text_features=None,
                         tokenizers=None,
                         dictionaries=None,
                         feature_calcers=None,
                         text_processing=None,
                         fixed_binary_splits=None)

PurposePurpose

Training and applying models for the classification problems. Provides compatibility with the scikit-learn tools.

The default optimized objective depends on various conditions:

• Logloss — The target has only two different values or the target_border parameter is not None.
• MultiClass — The target has more than two different values and the border_count parameter is None.

ParametersParameters

metadatametadata

DescriptionDescription

The key-value string pairs to store in the model's metadata storage after the training.

Default value

None

cat_featurescat_features

DescriptionDescription

A one-dimensional array of categorical columns indices (specified as integers) or names (specified as strings).

This array can contain both indices and names for different elements.

If any features in the cat_features parameter are specified as names instead of indices, feature names must be provided for the training dataset. Therefore, the type of the X parameter in the future calls of the fit function must be either catboost.Pool with defined feature names data or pandas.DataFrame with defined column names.

Default value

None (all features are either considered numerical or of other types if specified precisely)

text_featurestext_features

DescriptionDescription

A one-dimensional array of text columns indices (specified as integers) or names (specified as strings).

Use only if the data parameter is a two-dimensional feature matrix (has one of the following types: list, numpy.ndarray, pandas.DataFrame, pandas.Series).

If any elements in this array are specified as names instead of indices, names for all columns must be provided. To do this, either use the feature_names parameter of this constructor to explicitly specify them or pass a pandas.DataFrame with column names specified in the data parameter.

Default value

None (all features are either considered numerical or of other types if specified precisely)

See Python package training parameters for the full list of parameters.

AttributesAttributes

tree_count_tree_count_

Return the number of trees in the model.

This number can differ from the value specified in the --iterations training parameter in the following cases:

• The training is stopped by the overfitting detector.
• The --use-best-model training parameter is set to True.

feature_importances_feature_importances_

Return the calculated feature importances. The output data depends on the type of the model's loss function:

• Non-ranking loss functions — PredictionValuesChange
• Ranking loss functions — LossFunctionChange

random_seed_random_seed_

The random seed used for training.

learning_rate_learning_rate_

The learning rate used for training.

feature_names_feature_names_

The names of features in the dataset.

evals_result_evals_result_

Return the values of metrics calculated during the training.

best_score_best_score_

Return the best result for each metric calculated on each validation dataset.

best_iteration_best_iteration_

Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.

classes_classes_

Return the names of classes for classification models. An empty list is returned for all other models.

The order of classes in this list corresponds to the order of classes in resulting predictions.

MethodsMethods

fitfit

Train a model.

predictpredict

Apply the model to the given dataset.

predict_probapredict_proba

Apply the model to the given dataset to predict the probability that the object belongs to the given classes.

calc_leaf_indexescalc_leaf_indexes

Returns indexes of leafs to which objects from pool are mapped by model trees.

calc_feature_statisticscalc_feature_statistics

Calculate and plot a set of statistics for the chosen feature.

comparecompare

Draw train and evaluation metrics in Jupyter Notebook for two trained models.

copycopy

Copy the CatBoost object.

eval_metricseval_metrics

Calculate the specified metrics for the specified dataset.

get_all_paramsget_all_params

Return the values of all training parameters (including the ones that are not explicitly specified by users).

get_best_iterationget_best_iteration

Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.

get_best_scoreget_best_score

Return the best result for each metric calculated on each validation dataset.

get_bordersget_borders

Return the list of borders for numerical features.

get_evals_resultget_evals_result

Return the values of metrics calculated during the training.

get_feature_importanceget_feature_importance

Calculate and return the feature importances.

get_metadataget_metadata

Return a proxy object with metadata from the model's internal key-value string storage.

get_object_importanceget_object_importance

Calculate the effect of objects from the train dataset on the optimized metric values for the objects from the input dataset:

• Positive values reflect that the optimized metric increases.
• Negative values reflect that the optimized metric decreases.

get_paramget_param

Return the value of the given parameter if it is explicitly by the user before starting the training. If this parameter is used with the default value, this function returns None.

get_paramsget_params

Return the values of training parameters that are explicitly specified by the user. If all parameters are used with their default values, this function returns an empty dict.

get_probability_thresholdget_probability_threshold

Get a threshold for class separation in binary classification task for a trained model.

get_scale_and_biasget_scale_and_bias

Return the scale and bias of the model.

These values affect the results of applying the model, since the model prediction results are calculated as follows:
$\sum leaf\_values \cdot scale + bias$

get_test_evalget_test_eval

Return the formula values that were calculated for the objects from the validation dataset provided for training.

grid_searchgrid_search

A simple grid search over specified parameter values for a model.

is_fittedis_fitted

Check whether the model is trained.

load_modelload_model

Load the model from a file.

plot_predictionsplot_predictions

Sequentially vary the value of the specified features to put them into all buckets and calculate predictions for the input objects accordingly.

plot_treeplot_tree

Visualize the CatBoost decision trees.

randomized_searchrandomized_search

A simple randomized search on hyperparameters.

save_borderssave_borders

Save the model borders to a file.

save_modelsave_model

Save the model to a file.

scorescore

Calculate the Accuracy metric for the objects in the given dataset.

select_featuresselect_features

Select the best features from the dataset using the Recursive Feature Elimination algorithm.

set_feature_namesset_feature_names

Set names for all features in the model.

set_paramsset_params

Set the training parameters.

set_probability_thresholdset_probability_threshold

Set a threshold for class separation in binary classification task for a trained model.

set_scale_and_biasset_scale_and_bias

Set the scale and bias.

shrinkshrink

Shrink the model. Only trees with indices from the range [ntree_start, ntree_end) are kept.

staged_predictstaged_predict

Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i).

staged_predict_probastaged_predict_proba

Apply the model to the given dataset to predict the probability that the object belongs to the class and calculate the results taking into consideration only the trees in the range [0; i).