🕷️ 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 - CatBoostRanker - [Overview](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker) - [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_fit) - [predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_predict) - [Attributes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_attributes) - [calc\_leaf\_indexes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_calc_leaf_indexes) - [calc\_feature\_statistics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_calc_feature_statistics) - [compare](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_modelcompare) - [copy](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_copy) - [eval\_metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_eval-metrics) - [get\_all\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_all_params) - [get\_best\_iteration](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_best_iteration) - [get\_best\_score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_best_score) - [get\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_borders) - [get\_evals\_result](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_evals_result) - get\_feature\_importance - [get\_metadata](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_metadata) - [get\_object\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_object_importance) - [get\_param](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_param) - [get\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_params) - [get\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_scale_and_bias) - [get\_test\_eval](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_get_test_eval) - [grid\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_grid_search) - [is\_fitted](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_is_fitted) - [load\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_load_model) - [plot\_predictions](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_plot_predictions) - [plot\_tree](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_plot_tree) - [randomized\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_randomized_search) - [save\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_save_model) - [save\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_save_borders) - [score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score) - [select\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_select_features) - [set\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_set_scale_and_bias) - [set\_feature\_names](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_set_feature_names) - [set\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_set_params) - [shrink](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_shrink) - [staged\_predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_staged_predict) - [virtual\_ensembles\_predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_virtual_ensemblesranker_predict) - 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_catboostranker_score#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#parameters) - [X](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#x) - [y](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#y) - [group\_id](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#group_id) - [top](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#top) - [type](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#type) - [denominator](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#denominator) - [group\_weight](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#group_weight) - [thread\_count](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#thread_count) - [Type of return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#output-format) 1. Python package 2. [CatBoostRanker](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker) 3. score # score - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#parameters) - [X](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#x) - [y](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#y) - [group\_id](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#group_id) - [top](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#top) - [type](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#type) - [denominator](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#denominator) - [group\_weight](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#group_weight) - [thread\_count](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#thread_count) - [Type of return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_score#output-format) Calculate the NDCG@top [metric](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions) for the objects in the given dataset. ## Method call format ``` score(X, y=None, group_id=None, top=None, type=None, denominator=None, group_weight=None, thread_count=-1) ``` ## 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-dimensional array of numeric values. 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.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) **Default value** None **Supported processing units** CPU and GPU ### group\_id #### Description A ranking group. Note Do not use this parameter if the input training dataset (specified in the `X` parameter) type is catboost.Pool. **Possible types** - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) **Default value** None ### top #### Description NDCG, Number of top-ranked objects to calculate NDCG **Possible types** - unsigned integer, up to `pow(2, 32) / 2 - 1` **Default value** None ### type #### Description Metric type: Base or Exp. **Possible types** - str **Default value** None ### denominator #### Description Denominator type. **Possible types** - str **Default value** None ### group\_weight #### Description The weights of all objects within the defined groups from the input data in the form of one-dimensional array-like data. Used for calculating the final values of trees. By default, it is set to one for all objects in all groups. Only a `weight` or `group_weight` parameter can be used at the time. **Possible types** - list - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) **Default value** None ### thread\_count #### Description The number of threads to use. Optimizes the speed of execution. This parameter doesn't affect results. **Possible types** - int **Default value** \-1 (the number of threads is equal to the number of processor cores) ## Type of return value float ### Was the article helpful? Yes No Previous [save\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_save_borders) Next [select\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostranker_select_features) 

Calculate the NDCG@top [metric](https://catboost.ai/docs/en/concepts/loss-functions) for the objects in the given dataset. ## Method call format ``` score(X, y=None, group_id=None, top=None, type=None, denominator=None, group_weight=None, thread_count=-1) ``` ## 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-dimensional array of numeric values. 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.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) **Default value** None **Supported processing units** CPU and GPU ### group\_id #### Description A ranking group. Note Do not use this parameter if the input training dataset (specified in the `X` parameter) type is catboost.Pool. **Possible types** - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) **Default value** None ### top #### Description NDCG, Number of top-ranked objects to calculate NDCG **Possible types** - unsigned integer, up to `pow(2, 32) / 2 - 1` **Default value** None ### type #### Description Metric type: Base or Exp. **Possible types** - str **Default value** None ### denominator #### Description Denominator type. **Possible types** - str **Default value** None ### group\_weight #### Description The weights of all objects within the defined groups from the input data in the form of one-dimensional array-like data. Used for calculating the final values of trees. By default, it is set to one for all objects in all groups. Only a `weight` or `group_weight` parameter can be used at the time. **Possible types** - list - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) **Default value** None ### thread\_count #### Description The number of threads to use. Optimizes the speed of execution. This parameter doesn't affect results. **Possible types** - int **Default value** \-1 (the number of threads is equal to the number of processor cores) ## Type of return value float