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  -H 'Content-Type: text/plain' \
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  -H 'Authorization: Basic YXBpOg==' \
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{"found_url":"https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostregressor_attributes","crawl_time":1775583697,"first_indexed_time":1731946768,"http_code":200,"src_unparsed":"ai,catboost!\/docs\/en\/concepts\/python-reference_catboostregressor_attributes s443","src_root_hash":"17435841955170310369","history_drop_reason":null,"meta_title":"Attributes | CatBoost","meta_descriptions":["tree_count_ Purpose. Return the number of trees in the model."],"attrs_boilerpipe_text":"tree_count_\nPurpose\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.\nType\nint\nfeature_importances_\nPurpose\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\nIf the corresponding feature importance is not calculated the returned value is \nNone\n.\nUse the `` function to surely calculate the\nLossFunctionChange\nfeature importance.\nType\nnumpy.ndarray\nrandom_seed_\nPurpose\nThe random seed used for training.\nType\nint\nlearning_rate_\nPurpose\nThe learning rate used for training.\nType\nfloat\nfeature_names_\nPurpose\nThe names of features in the dataset.\nType\nlist\nevals_result_\nPurpose\nReturn the values of metrics calculated during the training.\nNote\nOnly the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output:\nPFound\nYetiRank\nNDCG\nYetiRankPairwise\nAUC\nNormalizedGini\nFilteredDCG\nDCG\nUse the\nhints=skip_train~false\nparameter to enable the calculation. See the\nEnable, disable and configure metrics calculation\nsection for more details.\nType\ndict\nOutput format:\n{pool_name: {metric_name_1-1: [value_1, value_2, .., value_N]}, .., {metric_name_1-M: [value_1, value_2, .., value_N]}}\nFor example:\n{'learn': {'Logloss': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782], 'AUC': [1.0, 1.0, 1.0], 'CrossEntropy': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782]}}\nbest_score_\nPurpose\nReturn the best result for each metric calculated on each validation dataset.\nNote\nOnly the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output:\nPFound\nYetiRank\nNDCG\nYetiRankPairwise\nAUC\nNormalizedGini\nFilteredDCG\nDCG\nUse the\nhints=skip_train~false\nparameter to enable the calculation. See the\nEnable, disable and configure metrics calculation\nsection for more details.\nType\ndict\nOutput format:\n{pool_name_1: {metric_1: value,..., metric_N: value}, ..., pool_name_M: {metric_1: value,..., metric_N: value}\nFor example:\n{\n'validation'\n: {\n'Logloss'\n: 0.6085537606941837,\n'AUC'\n: 0.0}}\nbest_iteration_\nPurpose\nReturn the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.\nType\nint or None if the validation dataset is not specified.","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\nAttributes\n\n## In this article:\n- [tree\\_count\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#tree_count_)\n- [feature\\_importances\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#feature_importances_)\n- [random\\_seed\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#random_seed_)\n- [learning\\_rate\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#learning_rate_)\n- [feature\\_names\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#feature_names_)\n- [evals\\_result\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#eval_result_)\n- [best\\_score\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#best_score)\n- [best\\_iteration\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#best_iteration)\n\n1. Python package\n2. [CatBoostRegressor](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor)\n3. Attributes\n\n# Attributes\n- [tree\\_count\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#tree_count_)\n- [feature\\_importances\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#feature_importances_)\n- [random\\_seed\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#random_seed_)\n- [learning\\_rate\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#learning_rate_)\n- [feature\\_names\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#feature_names_)\n- [evals\\_result\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#eval_result_)\n- [best\\_score\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#best_score)\n- [best\\_iteration\\_](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_attributes#best_iteration)\n\n## tree\\_count\\_\n#### Purpose\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#### Type\nint\n\n## feature\\_importances\\_\n#### Purpose\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\nIf the corresponding feature importance is not calculated the returned value is \"None\".\n\nUse the \\`` function to surely calculate the [LossFunctionChange](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/fstr#regular-feature-importances__lossfunctionchange) feature importance.\n\n#### Type\nnumpy.ndarray\n\n## random\\_seed\\_\n#### Purpose\nThe random seed used for training.\n\n#### Type\nint\n\n## learning\\_rate\\_\n#### Purpose\nThe learning rate used for training.\n\n#### Type\nfloat\n\n## feature\\_names\\_\n#### Purpose\nThe names of features in the dataset.\n\n#### Type\nlist\n\n## evals\\_result\\_\n#### Purpose\nReturn the values of metrics calculated during the training.\n\nNote\n\nOnly the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output:\n\n- PFound\n- YetiRank\n- NDCG\n- YetiRankPairwise\n- AUC\n- NormalizedGini\n- FilteredDCG\n- DCG\n\nUse the `hints=skip_train~false` parameter to enable the calculation. See the [Enable, disable and configure metrics calculation](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/loss-functions#enable-disable-configure-metrics) section for more details.\n#### Type\ndict\n\nOutput format:\n```\n{pool_name: {metric_name_1-1: [value_1, value_2, .., value_N]}, .., {metric_name_1-M: [value_1, value_2, .., value_N]}}\n```\nFor example:\n```\n{'learn': {'Logloss': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782], 'AUC': [1.0, 1.0, 1.0], 'CrossEntropy': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782]}}\n```\n## best\\_score\\_\n#### Purpose\nReturn the best result for each metric calculated on each validation dataset.\n\nNote\n\nOnly the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output:\n\n- PFound\n- YetiRank\n- NDCG\n- YetiRankPairwise\n- AUC\n- NormalizedGini\n- FilteredDCG\n- DCG\n\nUse the `hints=skip_train~false` parameter to enable the calculation. See the [Enable, disable and configure metrics calculation](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/loss-functions#enable-disable-configure-metrics) section for more details.\n#### Type\ndict\n\nOutput format:\n```\n{pool_name_1: {metric_1: value,..., metric_N: value}, ..., pool_name_M: {metric_1: value,..., metric_N: value}\n```\nFor example:\n```\n{'validation': {'Logloss': 0.6085537606941837, 'AUC': 0.0}}\n```\n## best\\_iteration\\_\n#### Purpose\nReturn the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.\n\n#### Type\nint or None if the validation dataset is not specified.\n\n### Was the article helpful?\nYes\n\nNo\n\nPrevious\n\n[predict](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_predict)\n\nNext\n\n[calc\\_leaf\\_indexes](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboost_calc_leaf_indexes)\n\n![](https:\/\/mc.yandex.ru\/watch\/60763294)","attrs_readable_markdown":"## tree\\_count\\_\n#### Purpose\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#### Type\nint\n\n## feature\\_importances\\_\n#### Purpose\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\nIf the corresponding feature importance is not calculated the returned value is \"None\".\n\nUse the \\`` function to surely calculate the [LossFunctionChange](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr#regular-feature-importances__lossfunctionchange) feature importance.\n\n#### Type\nnumpy.ndarray\n\n## random\\_seed\\_\n#### Purpose\nThe random seed used for training.\n\n#### Type\nint\n\n## learning\\_rate\\_\n#### Purpose\nThe learning rate used for training.\n\n#### Type\nfloat\n\n## feature\\_names\\_\n#### Purpose\nThe names of features in the dataset.\n\n#### Type\nlist\n\n## evals\\_result\\_\n#### Purpose\nReturn the values of metrics calculated during the training.\n\nNote\n\nOnly the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output:\n\n- PFound\n- YetiRank\n- NDCG\n- YetiRankPairwise\n- AUC\n- NormalizedGini\n- FilteredDCG\n- DCG\n\nUse the `hints=skip_train~false` parameter to enable the calculation. See the [Enable, disable and configure metrics calculation](https:\/\/catboost.ai\/docs\/en\/concepts\/loss-functions#enable-disable-configure-metrics) section for more details.\n#### Type\ndict\n\nOutput format:\n```\n{pool_name: {metric_name_1-1: [value_1, value_2, .., value_N]}, .., {metric_name_1-M: [value_1, value_2, .., value_N]}}\n```\nFor example:\n```\n{'learn': {'Logloss': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782], 'AUC': [1.0, 1.0, 1.0], 'CrossEntropy': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782]}}\n```\n## best\\_score\\_\n#### Purpose\nReturn the best result for each metric calculated on each validation dataset.\n\nNote\n\nOnly the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output:\n\n- PFound\n- YetiRank\n- NDCG\n- YetiRankPairwise\n- AUC\n- NormalizedGini\n- FilteredDCG\n- DCG\n\nUse the `hints=skip_train~false` parameter to enable the calculation. See the [Enable, disable and configure metrics calculation](https:\/\/catboost.ai\/docs\/en\/concepts\/loss-functions#enable-disable-configure-metrics) section for more details.\n#### Type\ndict\n\nOutput format:\n```\n{pool_name_1: {metric_1: value,..., metric_N: value}, ..., pool_name_M: {metric_1: value,..., metric_N: value}\n```\nFor example:\n```\n{'validation': {'Logloss': 0.6085537606941837, 'AUC': 0.0}}\n```\n## best\\_iteration\\_\n#### Purpose\nReturn the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.\n\n#### Type\nint or None if the validation dataset is not specified.","meta_canonical":null}

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URL	https://catboost.ai/docs/en/concepts/python-reference_catboostregressor_attributes
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Meta Title	Attributes \| CatBoost
Meta Description	tree_count_ Purpose. Return the number of trees in the model.
Meta Canonical	null
Boilerpipe Text	tree_count_ Purpose 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 . Type int feature_importances_ Purpose 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 If the corresponding feature importance is not calculated the returned value is None . Use the `` function to surely calculate the LossFunctionChange feature importance. Type numpy.ndarray random_seed_ Purpose The random seed used for training. Type int learning_rate_ Purpose The learning rate used for training. Type float feature_names_ Purpose The names of features in the dataset. Type list evals_result_ Purpose Return the values of metrics calculated during the training. Note Only the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output: PFound YetiRank NDCG YetiRankPairwise AUC NormalizedGini FilteredDCG DCG Use the hints=skip_train~false parameter to enable the calculation. See the Enable, disable and configure metrics calculation section for more details. Type dict Output format: {pool_name: {metric_name_1-1: [value_1, value_2, .., value_N]}, .., {metric_name_1-M: [value_1, value_2, .., value_N]}} For example: {'learn': {'Logloss': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782], 'AUC': [1.0, 1.0, 1.0], 'CrossEntropy': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782]}} best_score_ Purpose Return the best result for each metric calculated on each validation dataset. Note Only the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output: PFound YetiRank NDCG YetiRankPairwise AUC NormalizedGini FilteredDCG DCG Use the hints=skip_train~false parameter to enable the calculation. See the Enable, disable and configure metrics calculation section for more details. Type dict Output format: {pool_name_1: {metric_1: value,..., metric_N: value}, ..., pool_name_M: {metric_1: value,..., metric_N: value} For example: { 'validation' : { 'Logloss' : 0.6085537606941837, 'AUC' : 0.0}} best_iteration_ Purpose Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set. Type int or None if the validation dataset is not specified.
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) Attributes ## In this article: - [tree\_count\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#tree_count_) - [feature\_importances\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#feature_importances_) - [random\_seed\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#random_seed_) - [learning\_rate\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#learning_rate_) - [feature\_names\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#feature_names_) - [evals\_result\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#eval_result_) - [best\_score\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#best_score) - [best\_iteration\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#best_iteration) 1. Python package 2. [CatBoostRegressor](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor) 3. Attributes # Attributes - [tree\_count\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#tree_count_) - [feature\_importances\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#feature_importances_) - [random\_seed\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#random_seed_) - [learning\_rate\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#learning_rate_) - [feature\_names\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#feature_names_) - [evals\_result\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#eval_result_) - [best\_score\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#best_score) - [best\_iteration\_](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_attributes#best_iteration) ## tree\_count\_ #### Purpose 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". #### Type int ## feature\_importances\_ #### Purpose 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) If the corresponding feature importance is not calculated the returned value is "None". Use the \`` function to surely calculate the [LossFunctionChange](https://catboost.ai/docs/en/concepts/en/concepts/fstr#regular-feature-importances__lossfunctionchange) feature importance. #### Type numpy.ndarray ## random\_seed\_ #### Purpose The random seed used for training. #### Type int ## learning\_rate\_ #### Purpose The learning rate used for training. #### Type float ## feature\_names\_ #### Purpose The names of features in the dataset. #### Type list ## evals\_result\_ #### Purpose Return the values of metrics calculated during the training. Note Only the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output: - PFound - YetiRank - NDCG - YetiRankPairwise - AUC - NormalizedGini - FilteredDCG - DCG Use the `hints=skip_train~false` parameter to enable the calculation. See the [Enable, disable and configure metrics calculation](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions#enable-disable-configure-metrics) section for more details. #### Type dict Output format: ``` {pool_name: {metric_name_1-1: [value_1, value_2, .., value_N]}, .., {metric_name_1-M: [value_1, value_2, .., value_N]}} ``` For example: ``` {'learn': {'Logloss': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782], 'AUC': [1.0, 1.0, 1.0], 'CrossEntropy': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782]}} ``` ## best\_score\_ #### Purpose Return the best result for each metric calculated on each validation dataset. Note Only the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output: - PFound - YetiRank - NDCG - YetiRankPairwise - AUC - NormalizedGini - FilteredDCG - DCG Use the `hints=skip_train~false` parameter to enable the calculation. See the [Enable, disable and configure metrics calculation](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions#enable-disable-configure-metrics) section for more details. #### Type dict Output format: ``` {pool_name_1: {metric_1: value,..., metric_N: value}, ..., pool_name_M: {metric_1: value,..., metric_N: value} ``` For example: ``` {'validation': {'Logloss': 0.6085537606941837, 'AUC': 0.0}} ``` ## best\_iteration\_ #### Purpose Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set. #### Type int or None if the validation dataset is not specified. ### Was the article helpful? Yes No Previous [predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_predict) Next [calc\_leaf\_indexes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboost_calc_leaf_indexes) ![](https://mc.yandex.ru/watch/60763294)
Readable Markdown	## tree\_count\_ #### Purpose 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". #### Type int ## feature\_importances\_ #### Purpose 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) If the corresponding feature importance is not calculated the returned value is "None". Use the \`` function to surely calculate the [LossFunctionChange](https://catboost.ai/docs/en/concepts/fstr#regular-feature-importances__lossfunctionchange) feature importance. #### Type numpy.ndarray ## random\_seed\_ #### Purpose The random seed used for training. #### Type int ## learning\_rate\_ #### Purpose The learning rate used for training. #### Type float ## feature\_names\_ #### Purpose The names of features in the dataset. #### Type list ## evals\_result\_ #### Purpose Return the values of metrics calculated during the training. Note Only the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output: - PFound - YetiRank - NDCG - YetiRankPairwise - AUC - NormalizedGini - FilteredDCG - DCG Use the `hints=skip_train~false` parameter to enable the calculation. See the [Enable, disable and configure metrics calculation](https://catboost.ai/docs/en/concepts/loss-functions#enable-disable-configure-metrics) section for more details. #### Type dict Output format: ``` {pool_name: {metric_name_1-1: [value_1, value_2, .., value_N]}, .., {metric_name_1-M: [value_1, value_2, .., value_N]}} ``` For example: ``` {'learn': {'Logloss': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782], 'AUC': [1.0, 1.0, 1.0], 'CrossEntropy': [0.6720840012056274, 0.6476800666988386, 0.6284055381249782]}} ``` ## best\_score\_ #### Purpose Return the best result for each metric calculated on each validation dataset. Note Only the values of calculated metrics are output. The following metrics are not calculated by default for the training dataset and therefore these metrics are not output: - PFound - YetiRank - NDCG - YetiRankPairwise - AUC - NormalizedGini - FilteredDCG - DCG Use the `hints=skip_train~false` parameter to enable the calculation. See the [Enable, disable and configure metrics calculation](https://catboost.ai/docs/en/concepts/loss-functions#enable-disable-configure-metrics) section for more details. #### Type dict Output format: ``` {pool_name_1: {metric_1: value,..., metric_N: value}, ..., pool_name_M: {metric_1: value,..., metric_N: value} ``` For example: ``` {'validation': {'Logloss': 0.6085537606941837, 'AUC': 0.0}} ``` ## best\_iteration\_ #### Purpose Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set. #### Type int or None if the validation dataset is not specified.
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