🕷️ Crawler Inspector

[](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) score ## In this article: - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#parameters) - [X](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#x) - [y](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#y) - [Type of return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#output-format) 1. Python package 2. [CatBoostClassifier](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier) 3. score # score - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#parameters) - [X](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#x) - [y](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#y) - [Type of return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score#output-format) Calculate the Accuracy [metric](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions) for the objects in the given dataset. ## Method call format ``` score(X, y) ``` ## Parameters ### X #### Description The description is different for each group of possible types. **Possible types** catboost.Pool The input training dataset. Note If a nontrivial value of the `cat_features` parameter is specified in the constructor of this class, CatBoost checks the equivalence of categorical features indices specification from the constructor parameters and in this Pool class. list, numpy.ndarray, pandas.DataFrame, pandas.Series, polars.DataFrame The input training dataset in the form of a two-dimensional feature matrix. pandas.SparseDataFrame, scipy.sparse.spmatrix (all subclasses except dia\_matrix) The input training dataset in the form of a two-dimensional sparse feature matrix. **Default value** Required parameter ### y #### Description The target variables (in other words, the objects' label values) for the evaluation dataset. Must be in the form of a one- or two- dimensional array. The type of data in the array depends on the machine learning task being solved: - Binary classification One-dimensional array containing one of: - Booleans, integers or strings that represent the labels of the classes (only two unique values). - Numeric values. The interpretation of numeric values depends on the selected loss function: - Logloss — The value is considered a positive class if it is strictly greater than the value of the `target_border` training parameter. Otherwise, it is considered a negative class. - CrossEntropy — The value is interpreted as the probability that the dataset object belongs to the positive class. Possible values are in the range `[0; 1]`. - Multiclassification — One-dimensional array of integers or strings that represent the labels of the classes. - Multi label classification Two-dimensional array. The first index is for a label/class, the second index is for an object. Possible values depend on the selected loss function: - MultiLogloss — Only {0, 1} or {False, True} values are allowed that specify whether an object belongs to the class corresponding to the first index. - MultiCrossEntropy — Numerical values in the range `[0; 1]` that are interpreted as the probability that the dataset object belongs to the class corresponding to the first index. Note Do not use this parameter if the input training dataset (specified in the `X` parameter) type is catboost.Pool. **Possible types** - list - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) - [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) **Default value** None **Supported processing units** CPU and GPU ## Type of return value float ### Was the article helpful? Yes No Previous [save\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_save_model) Next [select\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_select_features) 

Calculate the Accuracy [metric](https://catboost.ai/docs/en/concepts/loss-functions) for the objects in the given dataset. ## Method call format ``` score(X, y) ``` ## Parameters ### X #### Description The description is different for each group of possible types. **Possible types** catboost.Pool The input training dataset. Note If a nontrivial value of the `cat_features` parameter is specified in the constructor of this class, CatBoost checks the equivalence of categorical features indices specification from the constructor parameters and in this Pool class. list, numpy.ndarray, pandas.DataFrame, pandas.Series, polars.DataFrame The input training dataset in the form of a two-dimensional feature matrix. pandas.SparseDataFrame, scipy.sparse.spmatrix (all subclasses except dia\_matrix) The input training dataset in the form of a two-dimensional sparse feature matrix. **Default value** Required parameter ### y #### Description The target variables (in other words, the objects' label values) for the evaluation dataset. Must be in the form of a one- or two- dimensional array. The type of data in the array depends on the machine learning task being solved: - Binary classification One-dimensional array containing one of: - Booleans, integers or strings that represent the labels of the classes (only two unique values). - Numeric values. The interpretation of numeric values depends on the selected loss function: - Logloss — The value is considered a positive class if it is strictly greater than the value of the `target_border` training parameter. Otherwise, it is considered a negative class. - CrossEntropy — The value is interpreted as the probability that the dataset object belongs to the positive class. Possible values are in the range `[0; 1]`. - Multiclassification — One-dimensional array of integers or strings that represent the labels of the classes. - Multi label classification Two-dimensional array. The first index is for a label/class, the second index is for an object. Possible values depend on the selected loss function: - MultiLogloss — Only {0, 1} or {False, True} values are allowed that specify whether an object belongs to the class corresponding to the first index. - MultiCrossEntropy — Numerical values in the range `[0; 1]` that are interpreted as the probability that the dataset object belongs to the class corresponding to the first index. Note Do not use this parameter if the input training dataset (specified in the `X` parameter) type is catboost.Pool. **Possible types** - list - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) - [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) **Default value** None **Supported processing units** CPU and GPU ## Type of return value float