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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_catboostregressor_score\')), getAhrefsUnparsedNoserviceFromURL(\'https://catboost.ai/docs/en/concepts/python-reference_catboostregressor_score\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostregressor_score","crawl_time":1775054216,"first_indexed_time":1731946768,"http_code":200,"src_unparsed":"ai,catboost!\/docs\/en\/concepts\/python-reference_catboostregressor_score s443","src_root_hash":"17435841955170310369","history_drop_reason":null,"meta_title":"score | CatBoost","meta_descriptions":["Calculate the R2 metric for the objects in the given dataset. Method call format. score(X, y). Parameters X Description."],"attrs_boilerpipe_text":"Calculate the R2\nmetric\nfor the objects in the given dataset.\nMethod call format\nscore(X, y)\nParameters\nX\nDescription\nThe description is different for each group of possible types.\nPossible types\ncatboost.Pool\nThe input training dataset.\nNote\nIf a nontrivial value of the\ncat_features\nparameter 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.\nlist, numpy.ndarray, pandas.DataFrame, pandas.Series, polars.DataFrame\nThe input training dataset in the form of a two-dimensional feature matrix.\npandas.SparseDataFrame, scipy.sparse.spmatrix (all subclasses except dia_matrix)\nThe input training dataset in the form of a two-dimensional sparse feature matrix.\nDefault value\nRequired parameter\ny\nDescription\nThe target variables (in other words, the objects' label values) for the evaluation dataset.\nMust 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:\nRegression — One-dimensional array of numeric values.\nMultiregression - Two-dimensional array of numeric values. The first index is for a dimension, the second index is for an object.\nNote\nDo not use this parameter if the input training dataset (specified in the\nX\nparameter) type is catboost.Pool.\nPossible types\nlist\nnumpy.ndarray\npandas.DataFrame\npandas.Series\npolars.Series\npolars.DataFrame\nDefault value\nNone\nSupported processing units\nCPU and GPU\nType of return value\nfloat","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  - CatBoostRanker\n  - CatBoostRegressor\n    - [Overview](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor)\n    - [fit](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_fit)\n    - [predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_predict)\n    - [Attributes](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_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_catboostregressor_calc_feature_statistics)\n    - [copy](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_copy)\n    - [compare](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare)\n    - [eval\\_metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_eval-metrics)\n    - [get\\_all\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_all_params)\n    - [get\\_best\\_iteration](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_best_iteration)\n    - [get\\_best\\_score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_best_score)\n    - [get\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_borders)\n    - [get\\_evals\\_result](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_evals_result)\n    - [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_feature_importance)\n    - [get\\_metadata](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_metadata)\n    - [get\\_object\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_object_importance)\n    - [get\\_param](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_param)\n    - [get\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_params)\n    - [get\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_scale_and_bias)\n    - [get\\_test\\_eval](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_get_test_eval)\n    - [grid\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_grid_search)\n    - [is\\_fitted](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_is_fitted)\n    - [load\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_load_model)\n    - [plot\\_predictions](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_plot_predictions)\n    - [plot\\_tree](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_plot_tree)\n    - [randomized\\_search](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_randomized_search)\n    - [save\\_borders](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_save_borders)\n    - [save\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_save_model)\n    - [score](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score)\n    - [select\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_select_features)\n    - [set\\_feature\\_names](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_set_feature_names)\n    - [set\\_params](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_set_params)\n    - [set\\_scale\\_and\\_bias](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_set_scale_and_bias)\n    - [shrink](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_shrink)\n    - [staged\\_predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict)\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\nscore\n\n## In this article:\n- [Method call format](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#call-format)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#parameters)\n- [X](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#x)\n- [y](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#y)\n- [Type of return value](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#output-format)\n\n1. Python package\n2. [CatBoostRegressor](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor)\n3. score\n\n# score\n- [Method call format](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#call-format)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#parameters)\n  - [X](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#x)\n  - [y](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#y)\n- [Type of return value](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_score#output-format)\n\nCalculate the R2 [metric](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/loss-functions) for the objects in the given dataset.\n\n## Method call format\n```\nscore(X, y)\n```\n## Parameters\n### X\n#### Description\nThe description is different for each group of possible types.\n\n**Possible types**\n\ncatboost.Pool\n\nThe input training dataset.\n\nNote\n\nIf 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.\n\nlist, numpy.ndarray, pandas.DataFrame, pandas.Series, polars.DataFrame\n\nThe input training dataset in the form of a two-dimensional feature matrix.\n\npandas.SparseDataFrame, scipy.sparse.spmatrix (all subclasses except dia\\_matrix)\n\nThe input training dataset in the form of a two-dimensional sparse feature matrix.\n\n**Default value**\n\nRequired parameter\n\n### y\n#### Description\nThe target variables (in other words, the objects' label values) for the evaluation dataset.\n\nMust 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:\n\n- Regression — One-dimensional array of numeric values.\n- Multiregression - Two-dimensional array of numeric values. The first index is for a dimension, the second index is for an object.\n\nNote\n\nDo not use this parameter if the input training dataset (specified in the `X` parameter) type is catboost.Pool.\n\n**Possible types**\n\n- list\n- numpy.ndarray\n- pandas.DataFrame\n- pandas.Series\n- [polars.Series](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/series\/index.html)\n- [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html)\n\n**Default value**\n\nNone\n\n**Supported processing units**\n\nCPU and GPU\n\n## Type of return value\nfloat\n\n### Was the article helpful?\nYes\n\nNo\n\nPrevious\n\n[save\\_model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_save_model)\n\nNext\n\n[select\\_features](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_select_features)\n\n![](https:\/\/mc.yandex.ru\/watch\/60763294)","attrs_readable_markdown":"Calculate the R2 [metric](https:\/\/catboost.ai\/docs\/en\/concepts\/loss-functions) for the objects in the given dataset.\n\n## Method call format\n```\nscore(X, y)\n```\n## Parameters\n### X\n#### Description\nThe description is different for each group of possible types.\n\n**Possible types**\n\ncatboost.Pool\n\nThe input training dataset.\n\nNote\n\nIf 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.\n\nlist, numpy.ndarray, pandas.DataFrame, pandas.Series, polars.DataFrame\n\nThe input training dataset in the form of a two-dimensional feature matrix.\n\npandas.SparseDataFrame, scipy.sparse.spmatrix (all subclasses except dia\\_matrix)\n\nThe input training dataset in the form of a two-dimensional sparse feature matrix.\n\n**Default value**\n\nRequired parameter\n\n### y\n#### Description\nThe target variables (in other words, the objects' label values) for the evaluation dataset.\n\nMust 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:\n\n- Regression — One-dimensional array of numeric values.\n- Multiregression - Two-dimensional array of numeric values. The first index is for a dimension, the second index is for an object.\n\nNote\n\nDo not use this parameter if the input training dataset (specified in the `X` parameter) type is catboost.Pool.\n\n**Possible types**\n\n- list\n- numpy.ndarray\n- pandas.DataFrame\n- pandas.Series\n- [polars.Series](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/series\/index.html)\n- [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html)\n\n**Default value**\n\nNone\n\n**Supported processing units**\n\nCPU and GPU\n\n## Type of return value\nfloat","meta_canonical":null}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

5 days ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0.2 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://catboost.ai/docs/en/concepts/python-reference_catboostregressor_score
Last Crawled	2026-04-01 14:36:56 (5 days ago)
First Indexed	2024-11-18 16:19:28 (1 year ago)
HTTP Status Code	200
Meta Title	score \| CatBoost
Meta Description	Calculate the R2 metric for the objects in the given dataset. Method call format. score(X, y). Parameters X Description.
Meta Canonical	null
Boilerpipe Text	Calculate the R2 metric 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: Regression — One-dimensional array of numeric values. Multiregression - Two-dimensional array of numeric values. The first index is for a dimension, the second index is for an object. 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 polars.DataFrame Default value None Supported processing units CPU and GPU Type of return value float
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 - CatBoostRanker - CatBoostRegressor - [Overview](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor) - [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_fit) - [predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_predict) - [Attributes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_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_catboostregressor_calc_feature_statistics) - [copy](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_copy) - [compare](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare) - [eval\_metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_eval-metrics) - [get\_all\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_all_params) - [get\_best\_iteration](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_best_iteration) - [get\_best\_score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_best_score) - [get\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_borders) - [get\_evals\_result](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_evals_result) - [get\_feature\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_feature_importance) - [get\_metadata](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_metadata) - [get\_object\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_object_importance) - [get\_param](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_param) - [get\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_params) - [get\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_scale_and_bias) - [get\_test\_eval](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_get_test_eval) - [grid\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_grid_search) - [is\_fitted](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_is_fitted) - [load\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_load_model) - [plot\_predictions](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_plot_predictions) - [plot\_tree](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_plot_tree) - [randomized\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_randomized_search) - [save\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_save_borders) - [save\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_save_model) - [score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score) - [select\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_select_features) - [set\_feature\_names](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_set_feature_names) - [set\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_set_params) - [set\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_set_scale_and_bias) - [shrink](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_shrink) - [staged\_predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict) - [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_catboostregressor_score#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score#parameters) - [X](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score#x) - [y](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score#y) - [Type of return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score#output-format) 1. Python package 2. [CatBoostRegressor](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor) 3. score # score - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score#parameters) - [X](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score#x) - [y](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score#y) - [Type of return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_score#output-format) Calculate the R2 [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: - Regression — One-dimensional array of numeric values. - Multiregression - Two-dimensional array of numeric values. The first index is for a dimension, the second index is for an object. 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? 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Readable Markdown	Calculate the R2 [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: - Regression — One-dimensional array of numeric values. - Multiregression - Two-dimensional array of numeric values. The first index is for a dimension, the second index is for an object. 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
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