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Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 169 (from laksa097)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa169.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier\')), getAhrefsUnparsedNoserviceFromURL(\'https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier","crawl_time":1775577801,"first_indexed_time":1731684979,"http_code":200,"src_unparsed":"ai,catboost!\/docs\/en\/concepts\/python-reference_catboostclassifier s443","src_root_hash":"17435841955170310369","history_drop_reason":null,"meta_title":"CatBoostClassifier | CatBoost","meta_descriptions":["class CatBoostClassifier (iterations= None, learning_rate= None, depth= None, l2_leaf_reg= None, model_size_reg= None, rsm= None, loss_function= None, border_co"],"attrs_boilerpipe_text":"class\nCatBoostClassifier\n(iterations=\nNone\n,\n                         learning_rate=\nNone\n,\n                         depth=\nNone\n,\n                         l2_leaf_reg=\nNone\n,\n                         model_size_reg=\nNone\n,\n                         rsm=\nNone\n,\n                         loss_function=\nNone\n,\n                         border_count=\nNone\n,\n                         feature_border_type=\nNone\n,\n                         per_float_feature_quantization=\nNone\n,\n                         input_borders=\nNone\n,\n                         output_borders=\nNone\n,\n                         fold_permutation_block=\nNone\n,\n                         od_pval=\nNone\n,\n                         od_wait=\nNone\n,\n                         od_type=\nNone\n,\n                         nan_mode=\nNone\n,\n                         counter_calc_method=\nNone\n,\n                         leaf_estimation_iterations=\nNone\n,\n                         leaf_estimation_method=\nNone\n,\n                         thread_count=\nNone\n,\n                         random_seed=\nNone\n,\n                         use_best_model=\nNone\n,\n                         verbose=\nNone\n,\n                         logging_level=\nNone\n,\n                         metric_period=\nNone\n,\n                         ctr_leaf_count_limit=\nNone\n,\n                         store_all_simple_ctr=\nNone\n,\n                         max_ctr_complexity=\nNone\n,\n                         has_time=\nNone\n,\n                         allow_const_label=\nNone\n,\n                         classes_count=\nNone\n,\n                         class_weights=\nNone\n,\n                         auto_class_weights=\nNone\n,\n                         one_hot_max_size=\nNone\n,\n                         random_strength=\nNone\n,\n                         name=\nNone\n,\n                         ignored_features=\nNone\n,\n                         train_dir=\nNone\n,\n                         custom_loss=\nNone\n,\n                         custom_metric=\nNone\n,\n                         eval_metric=\nNone\n,\n                         bagging_temperature=\nNone\n,\n                         save_snapshot=\nNone\n,\n                         snapshot_file=\nNone\n,\n                         snapshot_interval=\nNone\n,\n                         fold_len_multiplier=\nNone\n,\n                         used_ram_limit=\nNone\n,\n                         gpu_ram_part=\nNone\n,\n                         allow_writing_files=\nNone\n,\n                         final_ctr_computation_mode=\nNone\n,\n                         approx_on_full_history=\nNone\n,\n                         boosting_type=\nNone\n,\n                         simple_ctr=\nNone\n,\n                         combinations_ctr=\nNone\n,\n                         per_feature_ctr=\nNone\n,\n                         task_type=\nNone\n,\n                         device_config=\nNone\n,\n                         devices=\nNone\n,\n                         bootstrap_type=\nNone\n,\n                         subsample=\nNone\n,\n                         sampling_unit=\nNone\n,\n                         dev_score_calc_obj_block_size=\nNone\n,\n                         max_depth=\nNone\n,\n                         n_estimators=\nNone\n,\n                         num_boost_round=\nNone\n,\n                         num_trees=\nNone\n,\n                         colsample_bylevel=\nNone\n,\n                         random_state=\nNone\n,\n                         reg_lambda=\nNone\n,\n                         objective=\nNone\n,\n                         eta=\nNone\n,\n                         max_bin=\nNone\n,\n                         scale_pos_weight=\nNone\n,\n                         gpu_cat_features_storage=\nNone\n,\n                         data_partition=\nNone\nmetadata=\nNone\n,\n                         early_stopping_rounds=\nNone\n,\n                         cat_features=\nNone\n,\n                         grow_policy=\nNone\n,\n                         min_data_in_leaf=\nNone\n,\n                         min_child_samples=\nNone\n,\n                         max_leaves=\nNone\n,\n                         num_leaves=\nNone\n,\n                         score_function=\nNone\n,\n                         leaf_estimation_backtracking=\nNone\n,\n                         ctr_history_unit=\nNone\n,\n                         monotone_constraints=\nNone\n,\n                         feature_weights=\nNone\n,\n                         penalties_coefficient=\nNone\n,\n                         first_feature_use_penalties=\nNone\n,\n                         model_shrink_rate=\nNone\n,\n                         model_shrink_mode=\nNone\n,\n                         langevin=\nNone\n,\n                         diffusion_temperature=\nNone\n,\n                         posterior_sampling=\nNone\n,\n                         boost_from_average=\nNone\n,\n                         text_features=\nNone\n,\n                         tokenizers=\nNone\n,\n                         dictionaries=\nNone\n,\n                         feature_calcers=\nNone\n,\n                         text_processing=\nNone\n,\n                         fixed_binary_splits=\nNone\n)\nPurpose\nImplementation of\nthe scikit-learn estimator API\nfor CatBoost classification.\nSupports model training, inference and auxiliary calculations like feature importance.\nThe default optimized objective depends on various conditions:\nLogloss — The target has only two different values or the\ntarget_border\nparameter is not None.\nMultiClass — The target has more than two different values and the\ntarget_border\nparameter is None.\nParameters\nmetadata\nDescription\nThe key-value string pairs to store in the model's metadata storage after the training.\nDefault value\nNone\ncat_features\nDescription\nA one-dimensional array of categorical columns indices (specified as integers) or names (specified as strings).\nThis array can contain both indices and names for different elements.\nIf any features in the\ncat_features\nparameter are specified as names instead of indices, feature names must be provided for the training dataset. Therefore, the type of the\nX\nparameter in the future calls of the\nfit\nfunction must be either\ncatboost.Pool\nwith defined feature names data or pandas.DataFrame or\npolars.DataFrame\n.\nNote\nIf this parameter is not None and the training dataset passed as the value of the X parameter to the fit function of this class has the\ncatboost.Pool\ntype, CatBoost checks the equivalence of the categorical features indices specification in this object and the one in the\ncatboost.Pool\nobject.\nIf this parameter is not None, passing objects of the\ncatboost.FeaturesData\ntype as the X parameter to the fit function of this class is prohibited.\nDefault value\nNone (all features are either considered numerical or of other types if specified precisely)\ntext_features\nDescription\nA one-dimensional array of text columns indices (specified as integers) or names (specified as strings).\nUse only if the data parameter is a two-dimensional feature matrix (has one of the following types: list, numpy.ndarray, pandas.DataFrame, pandas.Series,\npolars.DataFrame\n).\nIf 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\nfeature_names\nparameter of this constructor to explicitly specify them or pass a pandas.DataFrame or\npolars.DataFrame\nwith column names specified in the data parameter.\nDefault value\nNone (all features are either considered numerical or of other types if specified precisely)\nSee \nPython package training parameters\nfor the full list of parameters.\nNote\nSome parameters duplicate the ones specified for the \nfit\nmethod. In these cases the values specified for the \nfit\nmethod take precedence.\nAttributes\ntree_count_\nReturn the number of trees in the model.\nThis number can differ from the value specified in the \n--iterations\ntraining parameter in the following cases:\nThe training is stopped by the\noverfitting detector\n.\nThe \n--use-best-model\ntraining parameter is set to\nTrue\n.\nfeature_importances_\nReturn the calculated\nfeature importances\n. The output data depends on the type of the model's loss function:\nNon-ranking loss functions —\nPredictionValuesChange\nRanking loss functions —\nLossFunctionChange\nrandom_seed_\nThe random seed used for training.\nlearning_rate_\nThe learning rate used for training.\nfeature_names_\nThe names of features in the dataset.\nevals_result_\nReturn the values of metrics calculated during the training.\nbest_score_\nReturn the best result for each metric calculated on each validation dataset.\nbest_iteration_\nReturn the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.\nclasses_\nReturn the names of classes for classification models. An empty list is returned for all other models.\nThe order of classes in this list corresponds to the order of classes in resulting predictions.\nMethods\nfit\nTrain a model.\npredict\nApply the model to the given dataset.\npredict_proba\nApply the model to the given dataset to predict the probability that the object belongs to the given classes.\ncalc_leaf_indexes\nReturns indexes of leafs to which objects from pool are mapped by model trees.\ncalc_feature_statistics\nCalculate and plot a set of statistics for the chosen feature.\ncompare\nDraw train and evaluation metrics in\nJupyter Notebook\nfor two trained models.\ncopy\nCopy the CatBoost object.\neval_metrics\nCalculate the specified metrics for the specified dataset.\nget_all_params\nReturn the values of all training parameters (including the ones that are not explicitly specified by users).\nget_best_iteration\nReturn the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.\nget_best_score\nReturn the best result for each metric calculated on each validation dataset.\nget_borders\nReturn the list of borders for numerical features.\nget_evals_result\nReturn the values of metrics calculated during the training.\nget_feature_importance\nCalculate and return the \nfeature importances\n.\nget_metadata\nReturn a proxy object with metadata from the model's internal key-value string storage.\nget_object_importance\nCalculate the effect of objects from the train dataset on the optimized metric values for the objects from the input dataset:\nPositive values reflect that the optimized metric increases.\nNegative values reflect that the optimized metric decreases.\nget_param\nReturn 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.\nget_params\nReturn 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.\nget_probability_threshold\nGet a threshold for class separation in binary classification task for a trained model.\nget_scale_and_bias\nReturn the scale and bias of the model.\nThese values affect the results of applying the model, since the model prediction results are calculated as follows:\n∑\nl\ne\na\nf\n_\nv\na\nl\nu\ne\ns\n⋅\ns\nc\na\nl\ne\n+\nb\ni\na\ns\n\\sum leaf\\_values \\cdot scale + bias\nget_test_eval\nReturn the formula values that were calculated for the objects from the validation dataset provided for training.\ngrid_search\nA simple grid search over specified parameter values for a model.\nis_fitted\nCheck whether the model is trained.\nload_model\nLoad the model from a file.\nplot_predictions\nSequentially vary the value of the specified features to put them into all buckets and calculate predictions for the input objects accordingly.\nplot_tree\nVisualize the CatBoost decision trees.\nrandomized_search\nA simple randomized search on hyperparameters.\nsave_borders\nSave the model borders to a file.\nsave_model\nSave the model to a file.\nscore\nCalculate the Accuracy\nmetric\nfor the objects in the given dataset.\nselect_features\nSelect the best features from the dataset using the\nRecursive Feature Elimination\nalgorithm.\nset_feature_names\nSet names for all features in the model.\nset_params\nSet the training parameters.\nset_probability_threshold\nSet a threshold for class separation in binary classification task for a trained model.\nset_scale_and_bias\nSet the scale and bias.\nshrink\nShrink the model. Only trees with indices from the range\n[ntree_start, ntree_end)\nare kept.\nstaged_predict\nApply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i).\nstaged_predict_proba\nApply 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).","attrs_markdown":"[![Logo icon](https:\/\/yastatic.net\/s3\/locdoc\/daas-static\/catboost\/71b237a322eec6f2889af0dae2a9c549.svg)](https:\/\/catboost.ai\/ \"CatBoost\")\n\n- Installation\n  - [Overview](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/installation)\n  - Python package installation\n  - CatBoost for Apache Spark installation\n  - R package installation\n  - Command-line version binary\n  - Build from source\n- Key Features\n- Training parameters\n- Python package\n  - [Quick start](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-quickstart)\n  - CatBoost\n  - CatBoostClassifier\n    - [Overview](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier)\n    - [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_fit)\n    - [predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_predict)\n    - [predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_predict_proba)\n    - [Attributes](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\n    - [calc\\_leaf\\_indexes](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboost_calc_leaf_indexes)\n    - [calc\\_feature\\_statistics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_calc_feature_statistics)\n    - [compare](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_modelcompare)\n    - [copy](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_copy)\n    - [eval\\_metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_eval-metrics)\n    - [get\\_all\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_all_params)\n    - [get\\_best\\_iteration](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_best_iteration)\n    - [get\\_best\\_score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_best_score)\n    - [get\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_borders)\n    - [get\\_evals\\_result](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_evals_result)\n    - [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_feature_importance)\n    - [get\\_metadata](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_metadata)\n    - [get\\_object\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_object_importance)\n    - [get\\_param](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_param)\n    - [get\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_params)\n    - [get\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_probability_threshold)\n    - [get\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_scale_and_bias)\n    - [get\\_test\\_eval](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_test_eval)\n    - [grid\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_grid_search)\n    - [is\\_fitted](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_is_fitted)\n    - [load\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_load_model)\n    - [plot\\_predictions](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_plot_predictions)\n    - [plot\\_tree](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_plot_tree)\n    - [randomized\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_randomized_search)\n    - [save\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_save_borders)\n    - [save\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_save_model)\n    - [score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_score)\n    - [select\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_select_features)\n    - [set\\_feature\\_names](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_set_feature_names)\n    - [set\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_set_params)\n    - [set\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_set_probability_threshold)\n    - [set\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_set_scale_and_bias)\n    - [shrink](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_shrink)\n    - [staged\\_predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_staged_predict)\n    - [staged\\_predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_staged_predict_proba)\n  - CatBoostRanker\n  - CatBoostRegressor\n  - [cv](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_cv)\n  - datasets\n  - FeaturesData\n  - MetricVisualizer\n  - Pool\n  - [sample\\_gaussian\\_process](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_sample_gaussian_process)\n  - [sum\\_models](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_sum_models)\n  - [to\\_classifier](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_to_classifier)\n  - [to\\_regressor](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_to_regressor)\n  - [train](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_train)\n  - Text processing\n  - utils\n  - [Usage examples](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-usages-examples)\n- CatBoost for Apache Spark\n- R package\n- Command-line version\n- Applying models\n- Objectives and metrics\n- Model analysis\n- Data format description\n- [Parameter tuning](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/parameter-tuning)\n- [Speeding up the training](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/speed-up-training)\n- Data visualization\n- Algorithm details\n- [FAQ](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/faq)\n- Educational materials\n- [Development and contributions](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/development-and-contributions)\n- [Contacts](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/contacts)\n\nCatBoostClassifier\n\n## In this article:\n- [Purpose](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#purpose)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#parameters)\n- [metadata](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#metadata)\n- [cat\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#cat_features)\n- [text\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#text_features)\n- [Attributes](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#attributes)\n- [tree\\_count\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#tree_count_)\n- [feature\\_importances\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#feature_importances_)\n- [random\\_seed\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#random_seed_)\n- [learning\\_rate\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#learning_rate_)\n- [feature\\_names\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#feature_names_)\n- [evals\\_result\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#evals_result_)\n- [best\\_score\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#best_score_)\n- [best\\_iteration\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#best_iteration_)\n- [classes\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#classes_)\n- [Methods](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#methods)\n- [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#fit)\n- [predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#predict)\n- [predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#predict_proba)\n- [calc\\_leaf\\_indexes](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#calc_leaf_indexes)\n- [calc\\_feature\\_statistics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#calc_feature_statistics)\n- [compare](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#compare)\n- [copy](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#copy)\n- [eval\\_metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#eval_metrics)\n- [get\\_all\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_all_params)\n- [get\\_best\\_iteration](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_best_iteration)\n- [get\\_best\\_score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_best_score)\n- [get\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_borders)\n- [get\\_evals\\_result](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_evals_result)\n- [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_feature_importance)\n- [get\\_metadata](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_metadata)\n- [get\\_object\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_object_importance)\n- [get\\_param](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_param)\n- [get\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_params)\n- [get\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_probability_threshold)\n- [get\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_scale_and_bias)\n- [get\\_test\\_eval](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_test_eval)\n- [grid\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#grid_search)\n- [is\\_fitted](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#is_fitted)\n- [load\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#load_model)\n- [plot\\_predictions](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#plot_predictions)\n- [plot\\_tree](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#plot_tree)\n- [randomized\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#randomized_search)\n- [save\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#save_borders)\n- [save\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#save_model)\n- [score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#score)\n- [select\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#select_features)\n- [set\\_feature\\_names](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#set_feature_names)\n- [set\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#set_params)\n- [set\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#set_probability_threshold)\n- [set\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#set_scale_and_bias)\n- [shrink](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#shrink)\n- [staged\\_predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#staged_predict)\n- [staged\\_predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#staged_predict_proba)\n\n1. Python package\n2. CatBoostClassifier\n3. Overview\n\n# CatBoostClassifier\n- [Purpose](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#purpose)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#parameters)\n  - [metadata](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#metadata)\n  - [cat\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#cat_features)\n  - [text\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#text_features)\n- [Attributes](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#attributes)\n  - [tree\\_count\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#tree_count_)\n  - [feature\\_importances\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#feature_importances_)\n  - [random\\_seed\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#random_seed_)\n  - [learning\\_rate\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#learning_rate_)\n  - [feature\\_names\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#feature_names_)\n  - [evals\\_result\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#evals_result_)\n  - [best\\_score\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#best_score_)\n  - [best\\_iteration\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#best_iteration_)\n  - [classes\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#classes_)\n- [Methods](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#methods)\n  - [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#fit)\n  - [predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#predict)\n  - [predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#predict_proba)\n  - [calc\\_leaf\\_indexes](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#calc_leaf_indexes)\n  - [calc\\_feature\\_statistics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#calc_feature_statistics)\n  - [compare](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#compare)\n  - [copy](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#copy)\n  - [eval\\_metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#eval_metrics)\n  - [get\\_all\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_all_params)\n  - [get\\_best\\_iteration](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_best_iteration)\n  - [get\\_best\\_score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_best_score)\n  - [get\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_borders)\n  - [get\\_evals\\_result](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_evals_result)\n  - [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_feature_importance)\n  - [get\\_metadata](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_metadata)\n  - [get\\_object\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_object_importance)\n  - [get\\_param](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_param)\n  - [get\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_params)\n  - [get\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_probability_threshold)\n  - [get\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_scale_and_bias)\n  - [get\\_test\\_eval](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#get_test_eval)\n  - [grid\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#grid_search)\n  - [is\\_fitted](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#is_fitted)\n  - [load\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#load_model)\n  - [plot\\_predictions](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#plot_predictions)\n  - [plot\\_tree](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#plot_tree)\n  - [randomized\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#randomized_search)\n  - [save\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#save_borders)\n  - [save\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#save_model)\n  - [score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#score)\n  - [select\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#select_features)\n  - [set\\_feature\\_names](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#set_feature_names)\n  - [set\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#set_params)\n  - [set\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#set_probability_threshold)\n  - [set\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#set_scale_and_bias)\n  - [shrink](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#shrink)\n  - [staged\\_predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#staged_predict)\n  - [staged\\_predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier#staged_predict_proba)\n\n```\nclass CatBoostClassifier(iterations=None,\n                         learning_rate=None,\n                         depth=None,\n                         l2_leaf_reg=None,\n                         model_size_reg=None,\n                         rsm=None,\n                         loss_function=None,\n                         border_count=None,\n                         feature_border_type=None,\n                         per_float_feature_quantization=None,\n                         input_borders=None,\n                         output_borders=None,\n                         fold_permutation_block=None,\n                         od_pval=None,\n                         od_wait=None,\n                         od_type=None,\n                         nan_mode=None,\n                         counter_calc_method=None,\n                         leaf_estimation_iterations=None,\n                         leaf_estimation_method=None,\n                         thread_count=None,\n                         random_seed=None,\n                         use_best_model=None,\n                         verbose=None,\n                         logging_level=None,\n                         metric_period=None,\n                         ctr_leaf_count_limit=None,\n                         store_all_simple_ctr=None,\n                         max_ctr_complexity=None,\n                         has_time=None,\n                         allow_const_label=None,\n                         classes_count=None,\n                         class_weights=None,\n                         auto_class_weights=None,\n                         one_hot_max_size=None,\n                         random_strength=None,\n                         name=None,\n                         ignored_features=None,\n                         train_dir=None,\n                         custom_loss=None,\n                         custom_metric=None,\n                         eval_metric=None,\n                         bagging_temperature=None,\n                         save_snapshot=None,\n                         snapshot_file=None,\n                         snapshot_interval=None,\n                         fold_len_multiplier=None,\n                         used_ram_limit=None,\n                         gpu_ram_part=None,\n                         allow_writing_files=None,\n                         final_ctr_computation_mode=None,\n                         approx_on_full_history=None,\n                         boosting_type=None,\n                         simple_ctr=None,\n                         combinations_ctr=None,\n                         per_feature_ctr=None,\n                         task_type=None,\n                         device_config=None,\n                         devices=None,\n                         bootstrap_type=None,\n                         subsample=None,\n                         sampling_unit=None,\n                         dev_score_calc_obj_block_size=None,\n                         max_depth=None,\n                         n_estimators=None,\n                         num_boost_round=None,\n                         num_trees=None,\n                         colsample_bylevel=None,\n                         random_state=None,\n                         reg_lambda=None,\n                         objective=None,\n                         eta=None,\n                         max_bin=None,\n                         scale_pos_weight=None,\n                         gpu_cat_features_storage=None,\n                         data_partition=None\n                         metadata=None,\n                         early_stopping_rounds=None,\n                         cat_features=None,\n                         grow_policy=None,\n                         min_data_in_leaf=None,\n                         min_child_samples=None,\n                         max_leaves=None,\n                         num_leaves=None,\n                         score_function=None,\n                         leaf_estimation_backtracking=None,\n                         ctr_history_unit=None,\n                         monotone_constraints=None,\n                         feature_weights=None,\n                         penalties_coefficient=None,\n                         first_feature_use_penalties=None,\n                         model_shrink_rate=None,\n                         model_shrink_mode=None,\n                         langevin=None,\n                         diffusion_temperature=None,\n                         posterior_sampling=None,\n                         boost_from_average=None,\n                         text_features=None,\n                         tokenizers=None,\n                         dictionaries=None,\n                         feature_calcers=None,\n                         text_processing=None,\n                         fixed_binary_splits=None)\n```\n## Purpose\nImplementation of [the scikit-learn estimator API](https:\/\/scikit-learn.org\/stable\/developers\/develop.html) for CatBoost classification.\n\nSupports model training, inference and auxiliary calculations like feature importance.\n\nThe default optimized objective depends on various conditions:\n\n- Logloss — The target has only two different values or the `target_border` parameter is not None.\n- MultiClass — The target has more than two different values and the `target_border` parameter is None.\n## Parameters\n### metadata\n#### Description\nThe key-value string pairs to store in the model's metadata storage after the training.\n\n**Default value**\n\nNone\n\n### cat\\_features\n#### Description\nA one-dimensional array of categorical columns indices (specified as integers) or names (specified as strings).\n\nThis array can contain both indices and names for different elements.\n\nIf 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](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_pool) with defined feature names data or pandas.DataFrame or [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html).\n\nNote\n\n- If this parameter is not None and the training dataset passed as the value of the X parameter to the fit function of this class has the [catboost.Pool](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_pool) type, CatBoost checks the equivalence of the categorical features indices specification in this object and the one in the [catboost.Pool](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_pool) object.\n- If this parameter is not None, passing objects of the [catboost.FeaturesData](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-features-data__desc) type as the X parameter to the fit function of this class is prohibited.\n\n**Default value**\n\nNone (all features are either considered numerical or of other types if specified precisely)\n\n### text\\_features\n#### Description\nA one-dimensional array of text columns indices (specified as integers) or names (specified as strings).\n\nUse only if the data parameter is a two-dimensional feature matrix (has one of the following types: list, numpy.ndarray, pandas.DataFrame, pandas.Series, [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html)  \n ).\n\nIf 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 or [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html) with column names specified in the data parameter.\n\n**Default value**\n\nNone (all features are either considered numerical or of other types if specified precisely)\n\nSee [Python package training parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/references\/training-parameters\/) for the full list of parameters.\n\nNote\n\nSome parameters duplicate the ones specified for the [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_fit) method. In these cases the values specified for the [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_fit) method take precedence.\n## Attributes\n### [tree\\_count\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the number of trees in the model.\n\nThis number can differ from the value specified in the `--iterations` training parameter in the following cases:\n\n- The training is stopped by the [overfitting detector](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/overfitting-detector).\n- The `--use-best-model` training parameter is set to \"True\".\n### [feature\\_importances\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the calculated [feature importances](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/fstr). The output data depends on the type of the model's loss function:\n\n- Non-ranking loss functions — [PredictionValuesChange](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/fstr#regular-feature-importance)\n- Ranking loss functions — [LossFunctionChange](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/fstr#regular-feature-importances__lossfunctionchange)\n### [random\\_seed\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\nThe random seed used for training.\n\n### [learning\\_rate\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\nThe learning rate used for training.\n\n### [feature\\_names\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\nThe names of features in the dataset.\n\n### [evals\\_result\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the values of metrics calculated during the training.\n\n### [best\\_score\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the best result for each metric calculated on each validation dataset.\n\n### [best\\_iteration\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.\n\n### [classes\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the names of classes for classification models. An empty list is returned for all other models.\n\nThe order of classes in this list corresponds to the order of classes in resulting predictions.\n\n## Methods\n### [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_fit)\nTrain a model.\n\n### [predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_predict)\nApply the model to the given dataset.\n\n### [predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_predict_proba)\nApply the model to the given dataset to predict the probability that the object belongs to the given classes.\n\n### [calc\\_leaf\\_indexes](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboost_calc_leaf_indexes)\nReturns indexes of leafs to which objects from pool are mapped by model trees.\n\n### [calc\\_feature\\_statistics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_calc_feature_statistics)\nCalculate and plot a set of statistics for the chosen feature.\n\n### [compare](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_modelcompare)\nDraw train and evaluation metrics in [Jupyter Notebook](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/features\/visualization_jupyter-notebook) for two trained models.\n\n### [copy](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_copy)\nCopy the CatBoost object.\n\n### [eval\\_metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_eval-metrics)\nCalculate the specified metrics for the specified dataset.\n\n### [get\\_all\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_all_params)\nReturn the values of all training parameters (including the ones that are not explicitly specified by users).\n\n### [get\\_best\\_iteration](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_best_iteration)\nReturn the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.\n\n### [get\\_best\\_score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_best_score)\nReturn the best result for each metric calculated on each validation dataset.\n\n### [get\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_borders)\nReturn the list of borders for numerical features.\n\n### [get\\_evals\\_result](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_evals_result)\nReturn the values of metrics calculated during the training.\n\n### [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_feature_importance)\nCalculate and return the [feature importances](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/fstr).\n\n### [get\\_metadata](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_metadata)\nReturn a proxy object with metadata from the model's internal key-value string storage.\n\n### [get\\_object\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_object_importance)\nCalculate the effect of objects from the train dataset on the optimized metric values for the objects from the input dataset:\n\n- Positive values reflect that the optimized metric increases.\n- Negative values reflect that the optimized metric decreases.\n### [get\\_param](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_param)\nReturn 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.\n\n### [get\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_params)\nReturn 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.\n\n### [get\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_probability_threshold)\nGet a threshold for class separation in binary classification task for a trained model.\n\n### [get\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_scale_and_bias)\nReturn the scale and bias of the model.\n\nThese values affect the results of applying the model, since the model prediction results are calculated as follows:  \n ∑ l e a f \\_ v a l u e s ⋅ s c a l e \\+ b i a s \\\\sum leaf\\\\\\_values \\\\cdot scale + bias ∑leaf\\_values⋅scale\\+bias\n\n### [get\\_test\\_eval](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_get_test_eval)\nReturn the formula values that were calculated for the objects from the validation dataset provided for training.\n\n### [grid\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboost_grid_search)\nA simple grid search over specified parameter values for a model.\n\n### [is\\_fitted](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_is_fitted)\nCheck whether the model is trained.\n\n### [load\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_load_model)\nLoad the model from a file.\n\n### [plot\\_predictions](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_plot_predictions)\nSequentially vary the value of the specified features to put them into all buckets and calculate predictions for the input objects accordingly.\n\n### [plot\\_tree](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_plot_tree)\nVisualize the CatBoost decision trees.\n\n### [randomized\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboost_randomized_search)\nA simple randomized search on hyperparameters.\n\n### [save\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_save_borders)\nSave the model borders to a file.\n\n### [save\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_save_model)\nSave the model to a file.\n\n### [score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_score)\nCalculate the Accuracy [metric](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/loss-functions) for the objects in the given dataset.\n\n### [select\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_select_features)\nSelect the best features from the dataset using the [Recursive Feature Elimination](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.RFE.html) algorithm.\n\n### [set\\_feature\\_names](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_set_feature_names)\nSet names for all features in the model.\n\n### [set\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_set_params)\nSet the training parameters.\n\n### [set\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_set_probability_threshold)\nSet a threshold for class separation in binary classification task for a trained model.\n\n### [set\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_set_scale_and_bias)\nSet the scale and bias.\n\n### [shrink](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_shrink)\nShrink the model. Only trees with indices from the range `[ntree_start, ntree_end)` are kept.\n\n### [staged\\_predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_staged_predict)\nApply the model to the given dataset and calculate the results taking into consideration only the trees in the range \\[0; i).\n\n### [staged\\_predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_staged_predict_proba)\nApply 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).\n\n### Was the article helpful?\nYes\n\nNo\n\nPrevious\n\n[virtual\\_ensembles\\_predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_virtual_ensembles_predict)\n\nNext\n\n[fit](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostclassifier_fit)\n\n![](https:\/\/mc.yandex.ru\/watch\/60763294)","attrs_readable_markdown":"```\nclass CatBoostClassifier(iterations=None,\n                         learning_rate=None,\n                         depth=None,\n                         l2_leaf_reg=None,\n                         model_size_reg=None,\n                         rsm=None,\n                         loss_function=None,\n                         border_count=None,\n                         feature_border_type=None,\n                         per_float_feature_quantization=None,\n                         input_borders=None,\n                         output_borders=None,\n                         fold_permutation_block=None,\n                         od_pval=None,\n                         od_wait=None,\n                         od_type=None,\n                         nan_mode=None,\n                         counter_calc_method=None,\n                         leaf_estimation_iterations=None,\n                         leaf_estimation_method=None,\n                         thread_count=None,\n                         random_seed=None,\n                         use_best_model=None,\n                         verbose=None,\n                         logging_level=None,\n                         metric_period=None,\n                         ctr_leaf_count_limit=None,\n                         store_all_simple_ctr=None,\n                         max_ctr_complexity=None,\n                         has_time=None,\n                         allow_const_label=None,\n                         classes_count=None,\n                         class_weights=None,\n                         auto_class_weights=None,\n                         one_hot_max_size=None,\n                         random_strength=None,\n                         name=None,\n                         ignored_features=None,\n                         train_dir=None,\n                         custom_loss=None,\n                         custom_metric=None,\n                         eval_metric=None,\n                         bagging_temperature=None,\n                         save_snapshot=None,\n                         snapshot_file=None,\n                         snapshot_interval=None,\n                         fold_len_multiplier=None,\n                         used_ram_limit=None,\n                         gpu_ram_part=None,\n                         allow_writing_files=None,\n                         final_ctr_computation_mode=None,\n                         approx_on_full_history=None,\n                         boosting_type=None,\n                         simple_ctr=None,\n                         combinations_ctr=None,\n                         per_feature_ctr=None,\n                         task_type=None,\n                         device_config=None,\n                         devices=None,\n                         bootstrap_type=None,\n                         subsample=None,\n                         sampling_unit=None,\n                         dev_score_calc_obj_block_size=None,\n                         max_depth=None,\n                         n_estimators=None,\n                         num_boost_round=None,\n                         num_trees=None,\n                         colsample_bylevel=None,\n                         random_state=None,\n                         reg_lambda=None,\n                         objective=None,\n                         eta=None,\n                         max_bin=None,\n                         scale_pos_weight=None,\n                         gpu_cat_features_storage=None,\n                         data_partition=None\n                         metadata=None,\n                         early_stopping_rounds=None,\n                         cat_features=None,\n                         grow_policy=None,\n                         min_data_in_leaf=None,\n                         min_child_samples=None,\n                         max_leaves=None,\n                         num_leaves=None,\n                         score_function=None,\n                         leaf_estimation_backtracking=None,\n                         ctr_history_unit=None,\n                         monotone_constraints=None,\n                         feature_weights=None,\n                         penalties_coefficient=None,\n                         first_feature_use_penalties=None,\n                         model_shrink_rate=None,\n                         model_shrink_mode=None,\n                         langevin=None,\n                         diffusion_temperature=None,\n                         posterior_sampling=None,\n                         boost_from_average=None,\n                         text_features=None,\n                         tokenizers=None,\n                         dictionaries=None,\n                         feature_calcers=None,\n                         text_processing=None,\n                         fixed_binary_splits=None)\n```\n## Purpose\nImplementation of [the scikit-learn estimator API](https:\/\/scikit-learn.org\/stable\/developers\/develop.html) for CatBoost classification.\n\nSupports model training, inference and auxiliary calculations like feature importance.\n\nThe default optimized objective depends on various conditions:\n\n- Logloss — The target has only two different values or the `target_border` parameter is not None.\n- MultiClass — The target has more than two different values and the `target_border` parameter is None.\n## Parameters\n### metadata\n#### Description\nThe key-value string pairs to store in the model's metadata storage after the training.\n\n**Default value**\n\nNone\n\n### cat\\_features\n#### Description\nA one-dimensional array of categorical columns indices (specified as integers) or names (specified as strings).\n\nThis array can contain both indices and names for different elements.\n\nIf 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](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_pool) with defined feature names data or pandas.DataFrame or [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html).\n\nNote\n\n- If this parameter is not None and the training dataset passed as the value of the X parameter to the fit function of this class has the [catboost.Pool](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_pool) type, CatBoost checks the equivalence of the categorical features indices specification in this object and the one in the [catboost.Pool](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_pool) object.\n- If this parameter is not None, passing objects of the [catboost.FeaturesData](https:\/\/catboost.ai\/docs\/en\/concepts\/python-features-data__desc) type as the X parameter to the fit function of this class is prohibited.\n\n**Default value**\n\nNone (all features are either considered numerical or of other types if specified precisely)\n\n### text\\_features\n#### Description\nA one-dimensional array of text columns indices (specified as integers) or names (specified as strings).\n\nUse only if the data parameter is a two-dimensional feature matrix (has one of the following types: list, numpy.ndarray, pandas.DataFrame, pandas.Series, [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html)  \n ).\n\nIf 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 or [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html) with column names specified in the data parameter.\n\n**Default value**\n\nNone (all features are either considered numerical or of other types if specified precisely)\n\nSee [Python package training parameters](https:\/\/catboost.ai\/docs\/en\/references\/training-parameters\/) for the full list of parameters.\n\nNote\n\nSome parameters duplicate the ones specified for the [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_fit) method. In these cases the values specified for the [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_fit) method take precedence.\n## Attributes\n### [tree\\_count\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the number of trees in the model.\n\nThis number can differ from the value specified in the `--iterations` training parameter in the following cases:\n\n- The training is stopped by the [overfitting detector](https:\/\/catboost.ai\/docs\/en\/concepts\/overfitting-detector).\n- The `--use-best-model` training parameter is set to \"True\".\n### [feature\\_importances\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the calculated [feature importances](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr). The output data depends on the type of the model's loss function:\n\n- Non-ranking loss functions — [PredictionValuesChange](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr#regular-feature-importance)\n- Ranking loss functions — [LossFunctionChange](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr#regular-feature-importances__lossfunctionchange)\n### [random\\_seed\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_attributes)\nThe random seed used for training.\n\n### [learning\\_rate\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_attributes)\nThe learning rate used for training.\n\n### [feature\\_names\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_attributes)\nThe names of features in the dataset.\n\n### [evals\\_result\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the values of metrics calculated during the training.\n\n### [best\\_score\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the best result for each metric calculated on each validation dataset.\n\n### [best\\_iteration\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.\n\n### [classes\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_attributes)\nReturn the names of classes for classification models. An empty list is returned for all other models.\n\nThe order of classes in this list corresponds to the order of classes in resulting predictions.\n\n## Methods\n### [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_fit)\nTrain a model.\n\n### [predict](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_predict)\nApply the model to the given dataset.\n\n### [predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_predict_proba)\nApply the model to the given dataset to predict the probability that the object belongs to the given classes.\n\n### [calc\\_leaf\\_indexes](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboost_calc_leaf_indexes)\nReturns indexes of leafs to which objects from pool are mapped by model trees.\n\n### [calc\\_feature\\_statistics](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_calc_feature_statistics)\nCalculate and plot a set of statistics for the chosen feature.\n\n### [compare](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_modelcompare)\nDraw train and evaluation metrics in [Jupyter Notebook](https:\/\/catboost.ai\/docs\/en\/features\/visualization_jupyter-notebook) for two trained models.\n\n### [copy](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_copy)\nCopy the CatBoost object.\n\n### [eval\\_metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_eval-metrics)\nCalculate the specified metrics for the specified dataset.\n\n### [get\\_all\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_all_params)\nReturn the values of all training parameters (including the ones that are not explicitly specified by users).\n\n### [get\\_best\\_iteration](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_best_iteration)\nReturn the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.\n\n### [get\\_best\\_score](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_best_score)\nReturn the best result for each metric calculated on each validation dataset.\n\n### [get\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_borders)\nReturn the list of borders for numerical features.\n\n### [get\\_evals\\_result](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_evals_result)\nReturn the values of metrics calculated during the training.\n\n### [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_feature_importance)\nCalculate and return the [feature importances](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr).\n\n### [get\\_metadata](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_metadata)\nReturn a proxy object with metadata from the model's internal key-value string storage.\n\n### [get\\_object\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_object_importance)\nCalculate the effect of objects from the train dataset on the optimized metric values for the objects from the input dataset:\n\n- Positive values reflect that the optimized metric increases.\n- Negative values reflect that the optimized metric decreases.\n### [get\\_param](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_param)\nReturn 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.\n\n### [get\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_params)\nReturn 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.\n\n### [get\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_probability_threshold)\nGet a threshold for class separation in binary classification task for a trained model.\n\n### [get\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_scale_and_bias)\nReturn the scale and bias of the model.\n\nThese values affect the results of applying the model, since the model prediction results are calculated as follows:  \n ∑ l e a f \\_ v a l u e s ⋅ s c a l e \\+ b i a s \\\\sum leaf\\\\\\_values \\\\cdot scale + bias\n\n### [get\\_test\\_eval](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_test_eval)\nReturn the formula values that were calculated for the objects from the validation dataset provided for training.\n\n### [grid\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboost_grid_search)\nA simple grid search over specified parameter values for a model.\n\n### [is\\_fitted](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_is_fitted)\nCheck whether the model is trained.\n\n### [load\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_load_model)\nLoad the model from a file.\n\n### [plot\\_predictions](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_plot_predictions)\nSequentially vary the value of the specified features to put them into all buckets and calculate predictions for the input objects accordingly.\n\n### [plot\\_tree](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_plot_tree)\nVisualize the CatBoost decision trees.\n\n### [randomized\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboost_randomized_search)\nA simple randomized search on hyperparameters.\n\n### [save\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_save_borders)\nSave the model borders to a file.\n\n### [save\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_save_model)\nSave the model to a file.\n\n### [score](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_score)\nCalculate the Accuracy [metric](https:\/\/catboost.ai\/docs\/en\/concepts\/loss-functions) for the objects in the given dataset.\n\n### [select\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_select_features)\nSelect the best features from the dataset using the [Recursive Feature Elimination](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.RFE.html) algorithm.\n\n### [set\\_feature\\_names](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_set_feature_names)\nSet names for all features in the model.\n\n### [set\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_set_params)\nSet the training parameters.\n\n### [set\\_probability\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_set_probability_threshold)\nSet a threshold for class separation in binary classification task for a trained model.\n\n### [set\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostregressor_set_scale_and_bias)\nSet the scale and bias.\n\n### [shrink](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_shrink)\nShrink the model. Only trees with indices from the range `[ntree_start, ntree_end)` are kept.\n\n### [staged\\_predict](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_staged_predict)\nApply the model to the given dataset and calculate the results taking into consideration only the trees in the range \\[0; i).\n\n### [staged\\_predict\\_proba](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_staged_predict_proba)\nApply 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).","meta_canonical":null}

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Meta Description	class CatBoostClassifier (iterations= None, learning_rate= None, depth= None, l2_leaf_reg= None, model_size_reg= None, rsm= None, loss_function= None, border_co
Meta Canonical	null
Boilerpipe Text	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 ) Purpose Implementation of the scikit-learn estimator API for CatBoost classification. Supports model training, inference and auxiliary calculations like feature importance. 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 target_border parameter is None. Parameters metadata Description The key-value string pairs to store in the model's metadata storage after the training. Default value None cat_features Description 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 or polars.DataFrame . Note If this parameter is not None and the training dataset passed as the value of the X parameter to the fit function of this class has the catboost.Pool type, CatBoost checks the equivalence of the categorical features indices specification in this object and the one in the catboost.Pool object. If this parameter is not None, passing objects of the catboost.FeaturesData type as the X parameter to the fit function of this class is prohibited. Default value None (all features are either considered numerical or of other types if specified precisely) text_features Description 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, polars.DataFrame ). 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 or polars.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. Note Some parameters duplicate the ones specified for the fit method. In these cases the values specified for the fit method take precedence. Attributes 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_ 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_ The random seed used for training. learning_rate_ The learning rate used for training. feature_names_ The names of features in the dataset. evals_result_ Return the values of metrics calculated during the training. best_score_ Return the best result for each metric calculated on each validation dataset. 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_ 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. Methods fit Train a model. predict Apply the model to the given dataset. predict_proba Apply the model to the given dataset to predict the probability that the object belongs to the given classes. calc_leaf_indexes Returns indexes of leafs to which objects from pool are mapped by model trees. calc_feature_statistics Calculate and plot a set of statistics for the chosen feature. compare Draw train and evaluation metrics in Jupyter Notebook for two trained models. copy Copy the CatBoost object. eval_metrics Calculate the specified metrics for the specified dataset. get_all_params Return the values of all training parameters (including the ones that are not explicitly specified by users). get_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_score Return the best result for each metric calculated on each validation dataset. get_borders Return the list of borders for numerical features. get_evals_result Return the values of metrics calculated during the training. get_feature_importance Calculate and return the feature importances . get_metadata Return a proxy object with metadata from the model's internal key-value string storage. get_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_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_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_threshold Get a threshold for class separation in binary classification task for a trained model. get_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: ∑ l e a f _ v a l u e s ⋅ s c a l e + b i a s \sum leaf\_values \cdot scale + bias get_test_eval Return the formula values that were calculated for the objects from the validation dataset provided for training. grid_search A simple grid search over specified parameter values for a model. is_fitted Check whether the model is trained. load_model Load the model from a file. plot_predictions Sequentially vary the value of the specified features to put them into all buckets and calculate predictions for the input objects accordingly. plot_tree Visualize the CatBoost decision trees. randomized_search A simple randomized search on hyperparameters. save_borders Save the model borders to a file. save_model Save the model to a file. score Calculate the Accuracy metric for the objects in the given dataset. select_features Select the best features from the dataset using the Recursive Feature Elimination algorithm. set_feature_names Set names for all features in the model. set_params Set the training parameters. set_probability_threshold Set a threshold for class separation in binary classification task for a trained model. set_scale_and_bias Set the scale and bias. shrink Shrink the model. Only trees with indices from the range [ntree_start, ntree_end) are kept. staged_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_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).
Markdown	[![Logo icon](https://yastatic.net/s3/locdoc/daas-static/catboost/71b237a322eec6f2889af0dae2a9c549.svg)](https://catboost.ai/ "CatBoost") - Installation - [Overview](https://catboost.ai/docs/en/concepts/en/concepts/installation) - Python package installation - CatBoost for Apache Spark installation - R package installation - Command-line version binary - Build from source - Key Features - Training parameters - Python package - [Quick start](https://catboost.ai/docs/en/concepts/en/concepts/python-quickstart) - CatBoost - CatBoostClassifier - [Overview](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier) - [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_fit) - [predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_predict) - [predict\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_predict_proba) - [Attributes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) - [calc\_leaf\_indexes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboost_calc_leaf_indexes) - [calc\_feature\_statistics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_calc_feature_statistics) - [compare](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare) - [copy](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_copy) - [eval\_metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_eval-metrics) - [get\_all\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_all_params) - [get\_best\_iteration](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_best_iteration) - [get\_best\_score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_best_score) - [get\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_borders) - [get\_evals\_result](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_evals_result) - [get\_feature\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_feature_importance) - [get\_metadata](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_metadata) - [get\_object\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_object_importance) - [get\_param](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_param) - [get\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_params) - [get\_probability\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_probability_threshold) - [get\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_scale_and_bias) - [get\_test\_eval](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_test_eval) - [grid\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_grid_search) - [is\_fitted](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_is_fitted) - [load\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_load_model) - [plot\_predictions](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_plot_predictions) - [plot\_tree](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_plot_tree) - [randomized\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_randomized_search) - [save\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_save_borders) - [save\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_save_model) - [score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score) - [select\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_select_features) - [set\_feature\_names](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_feature_names) - [set\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_params) - [set\_probability\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_probability_threshold) - [set\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_scale_and_bias) - [shrink](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_shrink) - [staged\_predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_staged_predict) - [staged\_predict\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_staged_predict_proba) - CatBoostRanker - CatBoostRegressor - [cv](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_cv) - datasets - FeaturesData - MetricVisualizer - Pool - [sample\_gaussian\_process](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_sample_gaussian_process) - [sum\_models](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_sum_models) - [to\_classifier](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_to_classifier) - [to\_regressor](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_to_regressor) - [train](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_train) - Text processing - utils - [Usage examples](https://catboost.ai/docs/en/concepts/en/concepts/python-usages-examples) - CatBoost for Apache Spark - R package - Command-line version - Applying models - Objectives and metrics - Model analysis - Data format description - [Parameter tuning](https://catboost.ai/docs/en/concepts/en/concepts/parameter-tuning) - [Speeding up the training](https://catboost.ai/docs/en/concepts/en/concepts/speed-up-training) - Data visualization - Algorithm details - [FAQ](https://catboost.ai/docs/en/concepts/en/concepts/faq) - Educational materials - [Development and contributions](https://catboost.ai/docs/en/concepts/en/concepts/development-and-contributions) - [Contacts](https://catboost.ai/docs/en/concepts/en/concepts/contacts) CatBoostClassifier ## In this article: - [Purpose](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#purpose) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#parameters) - [metadata](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#metadata) - [cat\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#cat_features) - [text\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#text_features) - [Attributes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#attributes) - [tree\_count\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#tree_count_) - [feature\_importances\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#feature_importances_) - [random\_seed\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#random_seed_) - [learning\_rate\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#learning_rate_) - [feature\_names\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#feature_names_) - [evals\_result\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#evals_result_) - [best\_score\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#best_score_) - [best\_iteration\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#best_iteration_) - [classes\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#classes_) - [Methods](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#methods) - [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#fit) - [predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#predict) - [predict\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#predict_proba) - [calc\_leaf\_indexes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#calc_leaf_indexes) - [calc\_feature\_statistics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#calc_feature_statistics) - [compare](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#compare) - [copy](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#copy) - [eval\_metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#eval_metrics) - [get\_all\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_all_params) - [get\_best\_iteration](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_best_iteration) - [get\_best\_score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_best_score) - [get\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_borders) - [get\_evals\_result](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_evals_result) - [get\_feature\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_feature_importance) - [get\_metadata](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_metadata) - [get\_object\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_object_importance) - [get\_param](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_param) - [get\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_params) - [get\_probability\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_probability_threshold) - [get\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_scale_and_bias) - [get\_test\_eval](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_test_eval) - [grid\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#grid_search) - [is\_fitted](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#is_fitted) - [load\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#load_model) - [plot\_predictions](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#plot_predictions) - [plot\_tree](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#plot_tree) - [randomized\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#randomized_search) - [save\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#save_borders) - [save\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#save_model) - [score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#score) - [select\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#select_features) - [set\_feature\_names](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#set_feature_names) - [set\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#set_params) - [set\_probability\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#set_probability_threshold) - [set\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#set_scale_and_bias) - [shrink](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#shrink) - [staged\_predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#staged_predict) - [staged\_predict\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#staged_predict_proba) 1. Python package 2. CatBoostClassifier 3. Overview # CatBoostClassifier - [Purpose](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#purpose) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#parameters) - [metadata](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#metadata) - [cat\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#cat_features) - [text\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#text_features) - [Attributes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#attributes) - [tree\_count\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#tree_count_) - [feature\_importances\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#feature_importances_) - [random\_seed\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#random_seed_) - [learning\_rate\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#learning_rate_) - [feature\_names\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#feature_names_) - [evals\_result\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#evals_result_) - [best\_score\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#best_score_) - [best\_iteration\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#best_iteration_) - [classes\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#classes_) - [Methods](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#methods) - [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#fit) - [predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#predict) - [predict\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#predict_proba) - [calc\_leaf\_indexes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#calc_leaf_indexes) - [calc\_feature\_statistics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#calc_feature_statistics) - [compare](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#compare) - [copy](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#copy) - [eval\_metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#eval_metrics) - [get\_all\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_all_params) - [get\_best\_iteration](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_best_iteration) - [get\_best\_score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_best_score) - [get\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_borders) - [get\_evals\_result](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_evals_result) - [get\_feature\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_feature_importance) - [get\_metadata](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_metadata) - [get\_object\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_object_importance) - [get\_param](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_param) - [get\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_params) - [get\_probability\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_probability_threshold) - [get\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_scale_and_bias) - [get\_test\_eval](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#get_test_eval) - [grid\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#grid_search) - [is\_fitted](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#is_fitted) - [load\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#load_model) - [plot\_predictions](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#plot_predictions) - [plot\_tree](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#plot_tree) - [randomized\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#randomized_search) - [save\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#save_borders) - [save\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#save_model) - [score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#score) - [select\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#select_features) - [set\_feature\_names](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#set_feature_names) - [set\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#set_params) - [set\_probability\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#set_probability_threshold) - [set\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#set_scale_and_bias) - [shrink](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#shrink) - [staged\_predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#staged_predict) - [staged\_predict\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier#staged_predict_proba) ``` 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) ``` ## Purpose Implementation of [the scikit-learn estimator API](https://scikit-learn.org/stable/developers/develop.html) for CatBoost classification. Supports model training, inference and auxiliary calculations like feature importance. 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 `target_border` parameter is None. ## Parameters ### metadata #### Description The key-value string pairs to store in the model's metadata storage after the training. Default value None ### cat\_features #### Description 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](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_pool) with defined feature names data or pandas.DataFrame or [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html). Note - If this parameter is not None and the training dataset passed as the value of the X parameter to the fit function of this class has the [catboost.Pool](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_pool) type, CatBoost checks the equivalence of the categorical features indices specification in this object and the one in the [catboost.Pool](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_pool) object. - If this parameter is not None, passing objects of the [catboost.FeaturesData](https://catboost.ai/docs/en/concepts/en/concepts/python-features-data__desc) type as the X parameter to the fit function of this class is prohibited. Default value None (all features are either considered numerical or of other types if specified precisely) ### text\_features #### Description 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, [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) ). 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 or [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) 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](https://catboost.ai/docs/en/concepts/en/references/training-parameters/) for the full list of parameters. Note Some parameters duplicate the ones specified for the [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_fit) method. In these cases the values specified for the [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_fit) method take precedence. ## Attributes ### [tree\_count\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) 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](https://catboost.ai/docs/en/concepts/en/concepts/overfitting-detector). - The `--use-best-model` training parameter is set to "True". ### [feature\_importances\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) Return the calculated [feature importances](https://catboost.ai/docs/en/concepts/en/concepts/fstr). The output data depends on the type of the model's loss function: - Non-ranking loss functions — [PredictionValuesChange](https://catboost.ai/docs/en/concepts/en/concepts/fstr#regular-feature-importance) - Ranking loss functions — [LossFunctionChange](https://catboost.ai/docs/en/concepts/en/concepts/fstr#regular-feature-importances__lossfunctionchange) ### [random\_seed\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) The random seed used for training. ### [learning\_rate\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) The learning rate used for training. ### [feature\_names\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) The names of features in the dataset. ### [evals\_result\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) Return the values of metrics calculated during the training. ### [best\_score\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) Return the best result for each metric calculated on each validation dataset. ### [best\_iteration\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set. ### [classes\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) 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. ## Methods ### [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_fit) Train a model. ### [predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_predict) Apply the model to the given dataset. ### [predict\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_predict_proba) Apply the model to the given dataset to predict the probability that the object belongs to the given classes. ### [calc\_leaf\_indexes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboost_calc_leaf_indexes) Returns indexes of leafs to which objects from pool are mapped by model trees. ### [calc\_feature\_statistics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_calc_feature_statistics) Calculate and plot a set of statistics for the chosen feature. ### [compare](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare) Draw train and evaluation metrics in [Jupyter Notebook](https://catboost.ai/docs/en/concepts/en/features/visualization_jupyter-notebook) for two trained models. ### [copy](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_copy) Copy the CatBoost object. ### [eval\_metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_eval-metrics) Calculate the specified metrics for the specified dataset. ### [get\_all\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_all_params) Return the values of all training parameters (including the ones that are not explicitly specified by users). ### [get\_best\_iteration](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_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\_score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_best_score) Return the best result for each metric calculated on each validation dataset. ### [get\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_borders) Return the list of borders for numerical features. ### [get\_evals\_result](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_evals_result) Return the values of metrics calculated during the training. ### [get\_feature\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_feature_importance) Calculate and return the [feature importances](https://catboost.ai/docs/en/concepts/en/concepts/fstr). ### [get\_metadata](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_metadata) Return a proxy object with metadata from the model's internal key-value string storage. ### [get\_object\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_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\_param](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_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\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_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\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_probability_threshold) Get a threshold for class separation in binary classification task for a trained model. ### [get\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_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: ∑ l e a f \_ v a l u e s ⋅ s c a l e \+ b i a s \\sum leaf\\\_values \\cdot scale + bias ∑leaf\_values⋅scale\+bias ### [get\_test\_eval](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_test_eval) Return the formula values that were calculated for the objects from the validation dataset provided for training. ### [grid\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboost_grid_search) A simple grid search over specified parameter values for a model. ### [is\_fitted](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_is_fitted) Check whether the model is trained. ### [load\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_load_model) Load the model from a file. ### [plot\_predictions](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_plot_predictions) Sequentially vary the value of the specified features to put them into all buckets and calculate predictions for the input objects accordingly. ### [plot\_tree](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_plot_tree) Visualize the CatBoost decision trees. ### [randomized\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboost_randomized_search) A simple randomized search on hyperparameters. ### [save\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_save_borders) Save the model borders to a file. ### [save\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_save_model) Save the model to a file. ### [score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score) Calculate the Accuracy [metric](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions) for the objects in the given dataset. ### [select\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_select_features) Select the best features from the dataset using the [Recursive Feature Elimination](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html) algorithm. ### [set\_feature\_names](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_feature_names) Set names for all features in the model. ### [set\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_params) Set the training parameters. ### [set\_probability\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_probability_threshold) Set a threshold for class separation in binary classification task for a trained model. ### [set\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_set_scale_and_bias) Set the scale and bias. ### [shrink](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_shrink) Shrink the model. Only trees with indices from the range `[ntree_start, ntree_end)` are kept. ### [staged\_predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_staged_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\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_staged_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). ### Was the article helpful? Yes No Previous [virtual\_ensembles\_predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_virtual_ensembles_predict) Next [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_fit) ![](https://mc.yandex.ru/watch/60763294)
Readable Markdown	``` 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) ``` ## Purpose Implementation of [the scikit-learn estimator API](https://scikit-learn.org/stable/developers/develop.html) for CatBoost classification. Supports model training, inference and auxiliary calculations like feature importance. 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 `target_border` parameter is None. ## Parameters ### metadata #### Description The key-value string pairs to store in the model's metadata storage after the training. Default value None ### cat\_features #### Description 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](https://catboost.ai/docs/en/concepts/python-reference_pool) with defined feature names data or pandas.DataFrame or [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html). Note - If this parameter is not None and the training dataset passed as the value of the X parameter to the fit function of this class has the [catboost.Pool](https://catboost.ai/docs/en/concepts/python-reference_pool) type, CatBoost checks the equivalence of the categorical features indices specification in this object and the one in the [catboost.Pool](https://catboost.ai/docs/en/concepts/python-reference_pool) object. - If this parameter is not None, passing objects of the [catboost.FeaturesData](https://catboost.ai/docs/en/concepts/python-features-data__desc) type as the X parameter to the fit function of this class is prohibited. Default value None (all features are either considered numerical or of other types if specified precisely) ### text\_features #### Description 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, [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) ). 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 or [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) 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](https://catboost.ai/docs/en/references/training-parameters/) for the full list of parameters. Note Some parameters duplicate the ones specified for the [fit](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_fit) method. In these cases the values specified for the [fit](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_fit) method take precedence. ## Attributes ### [tree\_count\_](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_attributes) 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](https://catboost.ai/docs/en/concepts/overfitting-detector). - The `--use-best-model` training parameter is set to "True". ### [feature\_importances\_](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_attributes) Return the calculated [feature importances](https://catboost.ai/docs/en/concepts/fstr). The output data depends on the type of the model's loss function: - Non-ranking loss functions — [PredictionValuesChange](https://catboost.ai/docs/en/concepts/fstr#regular-feature-importance) - Ranking loss functions — [LossFunctionChange](https://catboost.ai/docs/en/concepts/fstr#regular-feature-importances__lossfunctionchange) ### [random\_seed\_](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_attributes) The random seed used for training. ### [learning\_rate\_](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_attributes) The learning rate used for training. ### [feature\_names\_](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_attributes) The names of features in the dataset. ### [evals\_result\_](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_attributes) Return the values of metrics calculated during the training. ### [best\_score\_](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_attributes) Return the best result for each metric calculated on each validation dataset. ### [best\_iteration\_](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_attributes) Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set. ### [classes\_](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_attributes) 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. ## Methods ### [fit](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_fit) Train a model. ### [predict](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_predict) Apply the model to the given dataset. ### [predict\_proba](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_predict_proba) Apply the model to the given dataset to predict the probability that the object belongs to the given classes. ### [calc\_leaf\_indexes](https://catboost.ai/docs/en/concepts/python-reference_catboost_calc_leaf_indexes) Returns indexes of leafs to which objects from pool are mapped by model trees. ### [calc\_feature\_statistics](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_calc_feature_statistics) Calculate and plot a set of statistics for the chosen feature. ### [compare](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_modelcompare) Draw train and evaluation metrics in [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook) for two trained models. ### [copy](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_copy) Copy the CatBoost object. ### [eval\_metrics](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_eval-metrics) Calculate the specified metrics for the specified dataset. ### [get\_all\_params](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_all_params) Return the values of all training parameters (including the ones that are not explicitly specified by users). ### [get\_best\_iteration](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_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\_score](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_best_score) Return the best result for each metric calculated on each validation dataset. ### [get\_borders](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_borders) Return the list of borders for numerical features. ### [get\_evals\_result](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_evals_result) Return the values of metrics calculated during the training. ### [get\_feature\_importance](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_feature_importance) Calculate and return the [feature importances](https://catboost.ai/docs/en/concepts/fstr). ### [get\_metadata](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_metadata) Return a proxy object with metadata from the model's internal key-value string storage. ### [get\_object\_importance](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_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\_param](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_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\_params](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_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\_threshold](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_probability_threshold) Get a threshold for class separation in binary classification task for a trained model. ### [get\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_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: ∑ l e a f \_ v a l u e s ⋅ s c a l e \+ b i a s \\sum leaf\\\_values \\cdot scale + bias ### [get\_test\_eval](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_test_eval) Return the formula values that were calculated for the objects from the validation dataset provided for training. ### [grid\_search](https://catboost.ai/docs/en/concepts/python-reference_catboost_grid_search) A simple grid search over specified parameter values for a model. ### [is\_fitted](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_is_fitted) Check whether the model is trained. ### [load\_model](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_load_model) Load the model from a file. ### [plot\_predictions](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_plot_predictions) Sequentially vary the value of the specified features to put them into all buckets and calculate predictions for the input objects accordingly. ### [plot\_tree](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_plot_tree) Visualize the CatBoost decision trees. ### [randomized\_search](https://catboost.ai/docs/en/concepts/python-reference_catboost_randomized_search) A simple randomized search on hyperparameters. ### [save\_borders](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_save_borders) Save the model borders to a file. ### [save\_model](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_save_model) Save the model to a file. ### [score](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_score) Calculate the Accuracy [metric](https://catboost.ai/docs/en/concepts/loss-functions) for the objects in the given dataset. ### [select\_features](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_select_features) Select the best features from the dataset using the [Recursive Feature Elimination](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html) algorithm. ### [set\_feature\_names](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_set_feature_names) Set names for all features in the model. ### [set\_params](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_set_params) Set the training parameters. ### [set\_probability\_threshold](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_set_probability_threshold) Set a threshold for class separation in binary classification task for a trained model. ### [set\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/python-reference_catboostregressor_set_scale_and_bias) Set the scale and bias. ### [shrink](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_shrink) Shrink the model. Only trees with indices from the range `[ntree_start, ntree_end)` are kept. ### [staged\_predict](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_staged_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\_proba](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_staged_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).
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