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

Response:

{"found_url":"https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostregressor_modelcompare","crawl_time":1775601182,"first_indexed_time":1731946768,"http_code":200,"src_unparsed":"ai,catboost!\/docs\/en\/concepts\/python-reference_catboostregressor_modelcompare s443","src_root_hash":"17435841955170310369","history_drop_reason":null,"meta_title":"compare | CatBoost","meta_descriptions":["Draw train and evaluation metrics in Jupyter Notebook for two trained models. Method call format."],"attrs_boilerpipe_text":"Draw train and evaluation metrics in\nJupyter Notebook\nfor two trained models.\nMethod call format\ncompare(model,\n        data=\nNone\n,\n        metrics=\nNone\n,\n        ntree_start=\n0\n,\n        ntree_end=\n0\n,\n        eval_period=\n1\n,\n        thread_count=-\n1\n,\n        tmp_dir=\nNone\n,\n        log_cout=sys.stdout,\n        log_cerr=sys.stderr)\nParameters\nmodel\nDescription\nThe CatBoost model to compare with.\nPossible types\nCatBoost Model\nDefault value\nRequired parameter\nmetrics\nDescription\nThe list of metrics to be calculated.\nSupported metrics\nFor example, if the AUC and Logloss metrics should be calculated, use the following construction:\n[\n'Logloss'\n,\n'AUC'\n]\nPossible types\nlist of strings\nDefault value\nRequired parameter\ndata\nDescription\nA file or matrix with the input dataset, on which the compared metric values should be calculated.\nPossible types\ncatboost.Pool\nDefault value\nRequired parameter\nntree_start\nDescription\nTo reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to\n[ntree_start; ntree_end)\nand the\neval_period\nparameter to \nk\nto calculate metrics on every\nk\n-th iteration.\nThis parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based.\nPossible types\nint\nDefault value\n0\nntree_end\nDescription\nTo reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to\n[ntree_start; ntree_end)\nand the\neval_period\nparameter to \nk\nto calculate metrics on every\nk\n-th iteration.\nThis parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based.\nPossible types\nint\nDefault value\n0 (the index of the last tree to use equals to the number of trees in the\nmodel minus one)\neval_period\nDescription\nTo reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to\n[ntree_start; ntree_end)\nand the\neval_period\nparameter to \nk\nto calculate metrics on every\nk\n-th iteration.\nThis parameter defines the step to iterate over the range\n[\nntree_start\n;\nntree_end\n)\n. For example, let's assume that the following parameter values are set:\nntree_start\nis set 0\nntree_end\nis set to N (the total tree count)\neval_period\nis set to 2\nIn this case, the metrics are calculated for the following tree ranges:\n[0, 2)\n,\n[0, 4)\n, ... ,\n[0, N)\nPossible types\nint\nDefault value\n1 (the trees are applied sequentially: the first tree, then the first two\ntrees, etc.)\nthread_count\nDescription\nThe number of threads to use.\nOptimizes the speed of execution. This parameter doesn't affect results.\nPossible types\nint\nDefault value\n-1 (the number of threads is equal to the number of processor cores)\ntmp_dir\nDescription\nThe name of the temporary directory for intermediate results.\nPossible types\nString\nDefault value\nNone (the name is generated)\nlog_cout\nOutput stream or callback for logging.\nPossible types\ncallable Python object\npython object providing the\nwrite()\nmethod\nDefault value\nsys.stdout\nlog_cerr\nError stream or callback for logging.\nPossible types\ncallable Python object\npython object providing the\nwrite()\nmethod\nDefault value\nsys.stderr\nExamples\nfrom\ncatboost\nimport\nPool, CatBoostRegressor\n\ntrain_data = [[\n0\n,\n3\n],\n              [\n4\n,\n1\n],\n              [\n8\n,\n1\n],\n              [\n9\n,\n1\n]]\ntrain_labels = [\n0\n,\n0\n,\n1\n,\n1\n]\n\neval_data = [[\n1\n,\n3\n],\n             [\n4\n,\n2\n],\n             [\n8\n,\n2\n],\n             [\n8\n,\n3\n]]\n\neval_labels = [\n1\n,\n0\n,\n0\n,\n1\n]\n\ntrain_dataset = Pool(train_data, train_labels)\n\neval_dataset = Pool(eval_data, eval_labels)\n\nmodel1 = CatBoostRegressor(iterations=\n100\n, learning_rate=\n0.1\n)\nmodel1.fit(train_dataset, verbose=\nFalse\n)\n\nmodel2 = CatBoostRegressor(iterations=\n100\n, learning_rate=\n0.3\n)\nmodel2.fit(train_dataset, verbose=\nFalse\n)\n\nmodel1.compare(model2, eval_dataset, [\n'RMSE'\n])\nThe following is a chart plotted with \nJupyter Notebook\nfor the given example.","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\ncompare\n\n## In this article:\n- [Method call format](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#compare__method-call-format)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#parameters)\n- [model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#model)\n- [metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#metrics)\n- [data](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#data)\n- [ntree\\_start](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#ntree_start)\n- [ntree\\_end](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#ntree_end)\n- [eval\\_period](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#eval_period)\n- [thread\\_count](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#thread_count)\n- [tmp\\_dir](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#tmp_dir)\n- [log\\_cout](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#log_cout)\n- [log\\_cerr](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#log_cerr)\n- [Examples](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#examples)\n\n1. Python package\n2. [CatBoostRegressor](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor)\n3. compare\n\n# compare\n- [Method call format](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#compare__method-call-format)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#parameters)\n  - [model](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#model)\n  - [metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#metrics)\n  - [data](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#data)\n  - [ntree\\_start](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#ntree_start)\n  - [ntree\\_end](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#ntree_end)\n  - [eval\\_period](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#eval_period)\n  - [thread\\_count](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#thread_count)\n  - [tmp\\_dir](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#tmp_dir)\n  - [log\\_cout](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#log_cout)\n  - [log\\_cerr](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#log_cerr)\n- [Examples](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_modelcompare#examples)\n\nDraw train and evaluation metrics in [Jupyter Notebook](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/features\/visualization_jupyter-notebook) for two trained models.\n\n## Method call format\n```\ncompare(model,\n        data=None,\n        metrics=None,\n        ntree_start=0,\n        ntree_end=0,\n        eval_period=1,\n        thread_count=-1,\n        tmp_dir=None,\n        log_cout=sys.stdout,\n        log_cerr=sys.stderr)\n```\n## Parameters\n### model\n#### Description\nThe CatBoost model to compare with.\n\n**Possible types**\n\nCatBoost Model\n\n**Default value**\n\nRequired parameter\n\n### metrics\n#### Description\nThe list of metrics to be calculated.  \n [Supported metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/references\/custom-metric__supported-metrics)  \n For example, if the AUC and Logloss metrics should be calculated, use the following construction:\n```\n['Logloss', 'AUC']\n```\n**Possible types**\n\nlist of strings\n\n**Default value**\n\nRequired parameter\n\n### data\n#### Description\nA file or matrix with the input dataset, on which the compared metric values should be calculated.\n\n**Possible types**\n\ncatboost.Pool\n\n**Default value**\n\nRequired parameter\n\n### ntree\\_start\n#### Description\nTo reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\\-th iteration.\n\nThis parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based.\n\n**Possible types**\n\nint\n\n**Default value**\n\n0\n\n### ntree\\_end\n#### Description\nTo reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\\-th iteration.\n\nThis parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based.\n\n**Possible types**\n\nint\n\n**Default value**\n\n0 (the index of the last tree to use equals to the number of trees in the  \n model minus one)\n\n### eval\\_period\n#### Description\nTo reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\\-th iteration.\n\nThis parameter defines the step to iterate over the range `[`ntree\\_start`;`ntree\\_end`)`. For example, let's assume that the following parameter values are set:\n\n- `ntree_start` is set 0\n- `ntree_end` is set to N (the total tree count)\n- `eval_period` is set to 2\n\nIn this case, the metrics are calculated for the following tree ranges: `[0, 2)`, `[0, 4)`, ... , `[0, N)`\n\n**Possible types**\n\nint\n\n**Default value**\n\n1 (the trees are applied sequentially: the first tree, then the first two  \n trees, etc.)\n\n### thread\\_count\n#### Description\nThe number of threads to use.\n\nOptimizes the speed of execution. This parameter doesn't affect results.\n\n**Possible types**\n\nint\n\n**Default value**\n\n\\-1 (the number of threads is equal to the number of processor cores)\n\n### tmp\\_dir\n#### Description\nThe name of the temporary directory for intermediate results.\n\n**Possible types**\n\nString\n\n**Default value**\n\nNone (the name is generated)\n\n### log\\_cout\nOutput stream or callback for logging.\n\n**Possible types**\n\n- callable Python object\n- python object providing the `write()` method\n\n**Default value**\n\nsys.stdout\n\n### log\\_cerr\nError stream or callback for logging.\n\n**Possible types**\n\n- callable Python object\n- python object providing the `write()` method\n\n**Default value**\n\nsys.stderr\n\n## Examples\n```\nfrom catboost import Pool, CatBoostRegressor\n\ntrain_data = [[0, 3],\n              [4, 1],\n              [8, 1],\n              [9, 1]]\ntrain_labels = [0, 0, 1, 1]\n\neval_data = [[1, 3],\n             [4, 2],\n             [8, 2],\n             [8, 3]]\n\neval_labels = [1, 0, 0, 1]\n\ntrain_dataset = Pool(train_data, train_labels)\n\neval_dataset = Pool(eval_data, eval_labels)\n\nmodel1 = CatBoostRegressor(iterations=100, learning_rate=0.1)\nmodel1.fit(train_dataset, verbose=False)\n\nmodel2 = CatBoostRegressor(iterations=100, learning_rate=0.3)\nmodel2.fit(train_dataset, verbose=False)\n\nmodel1.compare(model2, eval_dataset, ['RMSE'])\n```\nThe following is a chart plotted with [Jupyter Notebook](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/features\/visualization_jupyter-notebook) for the given example.\n\n![](https:\/\/catboost.ai\/docs\/en\/concepts\/docs-assets\/catboost\/rev\/176123d0b3d555dac6641baf853bbb288710bec5\/en\/images\/interface__visualization-tools__jupyter__comparemodel__catboostregressor.png)\n\n### Was the article helpful?\nYes\n\nNo\n\nPrevious\n\n[copy](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_copy)\n\nNext\n\n[eval\\_metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_eval-metrics)\n\n![](https:\/\/mc.yandex.ru\/watch\/60763294)","attrs_readable_markdown":"Draw train and evaluation metrics in [Jupyter Notebook](https:\/\/catboost.ai\/docs\/en\/features\/visualization_jupyter-notebook) for two trained models.\n\n## Method call format\n```\ncompare(model,\n        data=None,\n        metrics=None,\n        ntree_start=0,\n        ntree_end=0,\n        eval_period=1,\n        thread_count=-1,\n        tmp_dir=None,\n        log_cout=sys.stdout,\n        log_cerr=sys.stderr)\n```\n## Parameters\n### model\n#### Description\nThe CatBoost model to compare with.\n\n**Possible types**\n\nCatBoost Model\n\n**Default value**\n\nRequired parameter\n\n### metrics\n#### Description\nThe list of metrics to be calculated.  \n [Supported metrics](https:\/\/catboost.ai\/docs\/en\/references\/custom-metric__supported-metrics)  \n For example, if the AUC and Logloss metrics should be calculated, use the following construction:\n```\n['Logloss', 'AUC']\n```\n**Possible types**\n\nlist of strings\n\n**Default value**\n\nRequired parameter\n\n### data\n#### Description\nA file or matrix with the input dataset, on which the compared metric values should be calculated.\n\n**Possible types**\n\ncatboost.Pool\n\n**Default value**\n\nRequired parameter\n\n### ntree\\_start\n#### Description\nTo reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\\-th iteration.\n\nThis parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based.\n\n**Possible types**\n\nint\n\n**Default value**\n\n0\n\n### ntree\\_end\n#### Description\nTo reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\\-th iteration.\n\nThis parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based.\n\n**Possible types**\n\nint\n\n**Default value**\n\n0 (the index of the last tree to use equals to the number of trees in the  \n model minus one)\n\n### eval\\_period\n#### Description\nTo reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\\-th iteration.\n\nThis parameter defines the step to iterate over the range `[`ntree\\_start`;`ntree\\_end`)`. For example, let's assume that the following parameter values are set:\n\n- `ntree_start` is set 0\n- `ntree_end` is set to N (the total tree count)\n- `eval_period` is set to 2\n\nIn this case, the metrics are calculated for the following tree ranges: `[0, 2)`, `[0, 4)`, ... , `[0, N)`\n\n**Possible types**\n\nint\n\n**Default value**\n\n1 (the trees are applied sequentially: the first tree, then the first two  \n trees, etc.)\n\n### thread\\_count\n#### Description\nThe number of threads to use.\n\nOptimizes the speed of execution. This parameter doesn't affect results.\n\n**Possible types**\n\nint\n\n**Default value**\n\n\\-1 (the number of threads is equal to the number of processor cores)\n\n### tmp\\_dir\n#### Description\nThe name of the temporary directory for intermediate results.\n\n**Possible types**\n\nString\n\n**Default value**\n\nNone (the name is generated)\n\n### log\\_cout\nOutput stream or callback for logging.\n\n**Possible types**\n\n- callable Python object\n- python object providing the `write()` method\n\n**Default value**\n\nsys.stdout\n\n### log\\_cerr\nError stream or callback for logging.\n\n**Possible types**\n\n- callable Python object\n- python object providing the `write()` method\n\n**Default value**\n\nsys.stderr\n\n## Examples\n```\nfrom catboost import Pool, CatBoostRegressor\n\ntrain_data = [[0, 3],\n              [4, 1],\n              [8, 1],\n              [9, 1]]\ntrain_labels = [0, 0, 1, 1]\n\neval_data = [[1, 3],\n             [4, 2],\n             [8, 2],\n             [8, 3]]\n\neval_labels = [1, 0, 0, 1]\n\ntrain_dataset = Pool(train_data, train_labels)\n\neval_dataset = Pool(eval_data, eval_labels)\n\nmodel1 = CatBoostRegressor(iterations=100, learning_rate=0.1)\nmodel1.fit(train_dataset, verbose=False)\n\nmodel2 = CatBoostRegressor(iterations=100, learning_rate=0.3)\nmodel2.fit(train_dataset, verbose=False)\n\nmodel1.compare(model2, eval_dataset, ['RMSE'])\n```\nThe following is a chart plotted with [Jupyter Notebook](https:\/\/catboost.ai\/docs\/en\/features\/visualization_jupyter-notebook) for the given example.\n\n![](https:\/\/catboost.ai\/docs\/docs-assets\/catboost\/rev\/176123d0b3d555dac6641baf853bbb288710bec5\/en\/images\/interface__visualization-tools__jupyter__comparemodel__catboostregressor.png)","meta_canonical":null}

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Boilerpipe Text	Draw train and evaluation metrics in Jupyter Notebook for two trained models. Method call format compare(model, data= None , metrics= None , ntree_start= 0 , ntree_end= 0 , eval_period= 1 , thread_count=- 1 , tmp_dir= None , log_cout=sys.stdout, log_cerr=sys.stderr) Parameters model Description The CatBoost model to compare with. Possible types CatBoost Model Default value Required parameter metrics Description The list of metrics to be calculated. Supported metrics For example, if the AUC and Logloss metrics should be calculated, use the following construction: [ 'Logloss' , 'AUC' ] Possible types list of strings Default value Required parameter data Description A file or matrix with the input dataset, on which the compared metric values should be calculated. Possible types catboost.Pool Default value Required parameter ntree_start Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to [ntree_start; ntree_end) and the eval_period parameter to k to calculate metrics on every k -th iteration. This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based. Possible types int Default value 0 ntree_end Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to [ntree_start; ntree_end) and the eval_period parameter to k to calculate metrics on every k -th iteration. This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based. Possible types int Default value 0 (the index of the last tree to use equals to the number of trees in the model minus one) eval_period Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to [ntree_start; ntree_end) and the eval_period parameter to k to calculate metrics on every k -th iteration. This parameter defines the step to iterate over the range [ ntree_start ; ntree_end ) . For example, let's assume that the following parameter values are set: ntree_start is set 0 ntree_end is set to N (the total tree count) eval_period is set to 2 In this case, the metrics are calculated for the following tree ranges: [0, 2) , [0, 4) , ... , [0, N) Possible types int Default value 1 (the trees are applied sequentially: the first tree, then the first two trees, etc.) 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) tmp_dir Description The name of the temporary directory for intermediate results. Possible types String Default value None (the name is generated) log_cout Output stream or callback for logging. Possible types callable Python object python object providing the write() method Default value sys.stdout log_cerr Error stream or callback for logging. Possible types callable Python object python object providing the write() method Default value sys.stderr Examples from catboost import Pool, CatBoostRegressor train_data = [[ 0 , 3 ], [ 4 , 1 ], [ 8 , 1 ], [ 9 , 1 ]] train_labels = [ 0 , 0 , 1 , 1 ] eval_data = [[ 1 , 3 ], [ 4 , 2 ], [ 8 , 2 ], [ 8 , 3 ]] eval_labels = [ 1 , 0 , 0 , 1 ] train_dataset = Pool(train_data, train_labels) eval_dataset = Pool(eval_data, eval_labels) model1 = CatBoostRegressor(iterations= 100 , learning_rate= 0.1 ) model1.fit(train_dataset, verbose= False ) model2 = CatBoostRegressor(iterations= 100 , learning_rate= 0.3 ) model2.fit(train_dataset, verbose= False ) model1.compare(model2, eval_dataset, [ 'RMSE' ]) The following is a chart plotted with Jupyter Notebook for the given example.
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) compare ## In this article: - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#compare__method-call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#parameters) - [model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#model) - [metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#metrics) - [data](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#data) - [ntree\_start](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#ntree_start) - [ntree\_end](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#ntree_end) - [eval\_period](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#eval_period) - [thread\_count](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#thread_count) - [tmp\_dir](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#tmp_dir) - [log\_cout](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#log_cout) - [log\_cerr](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#log_cerr) - [Examples](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#examples) 1. Python package 2. [CatBoostRegressor](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor) 3. compare # compare - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#compare__method-call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#parameters) - [model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#model) - [metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#metrics) - [data](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#data) - [ntree\_start](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#ntree_start) - [ntree\_end](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#ntree_end) - [eval\_period](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#eval_period) - [thread\_count](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#thread_count) - [tmp\_dir](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#tmp_dir) - [log\_cout](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#log_cout) - [log\_cerr](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#log_cerr) - [Examples](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_modelcompare#examples) Draw train and evaluation metrics in [Jupyter Notebook](https://catboost.ai/docs/en/concepts/en/features/visualization_jupyter-notebook) for two trained models. ## Method call format ``` compare(model, data=None, metrics=None, ntree_start=0, ntree_end=0, eval_period=1, thread_count=-1, tmp_dir=None, log_cout=sys.stdout, log_cerr=sys.stderr) ``` ## Parameters ### model #### Description The CatBoost model to compare with. Possible types CatBoost Model Default value Required parameter ### metrics #### Description The list of metrics to be calculated. [Supported metrics](https://catboost.ai/docs/en/concepts/en/references/custom-metric__supported-metrics) For example, if the AUC and Logloss metrics should be calculated, use the following construction: ``` ['Logloss', 'AUC'] ``` Possible types list of strings Default value Required parameter ### data #### Description A file or matrix with the input dataset, on which the compared metric values should be calculated. Possible types catboost.Pool Default value Required parameter ### ntree\_start #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to k to calculate metrics on every k\-th iteration. This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based. Possible types int Default value 0 ### ntree\_end #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to k to calculate metrics on every k\-th iteration. This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based. Possible types int Default value 0 (the index of the last tree to use equals to the number of trees in the model minus one) ### eval\_period #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to k to calculate metrics on every k\-th iteration. This parameter defines the step to iterate over the range `[`ntree\_start`;`ntree\_end`)`. For example, let's assume that the following parameter values are set: - `ntree_start` is set 0 - `ntree_end` is set to N (the total tree count) - `eval_period` is set to 2 In this case, the metrics are calculated for the following tree ranges: `[0, 2)`, `[0, 4)`, ... , `[0, N)` Possible types int Default value 1 (the trees are applied sequentially: the first tree, then the first two trees, etc.) ### 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) ### tmp\_dir #### Description The name of the temporary directory for intermediate results. Possible types String Default value None (the name is generated) ### log\_cout Output stream or callback for logging. Possible types - callable Python object - python object providing the `write()` method Default value sys.stdout ### log\_cerr Error stream or callback for logging. Possible types - callable Python object - python object providing the `write()` method Default value sys.stderr ## Examples ``` from catboost import Pool, CatBoostRegressor train_data = [[0, 3], [4, 1], [8, 1], [9, 1]] train_labels = [0, 0, 1, 1] eval_data = [[1, 3], [4, 2], [8, 2], [8, 3]] eval_labels = [1, 0, 0, 1] train_dataset = Pool(train_data, train_labels) eval_dataset = Pool(eval_data, eval_labels) model1 = CatBoostRegressor(iterations=100, learning_rate=0.1) model1.fit(train_dataset, verbose=False) model2 = CatBoostRegressor(iterations=100, learning_rate=0.3) model2.fit(train_dataset, verbose=False) model1.compare(model2, eval_dataset, ['RMSE']) ``` The following is a chart plotted with [Jupyter Notebook](https://catboost.ai/docs/en/concepts/en/features/visualization_jupyter-notebook) for the given example. ![](https://catboost.ai/docs/en/concepts/docs-assets/catboost/rev/176123d0b3d555dac6641baf853bbb288710bec5/en/images/interface__visualization-tools__jupyter__comparemodel__catboostregressor.png) ### Was the article helpful? Yes No Previous [copy](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_copy) Next [eval\_metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_eval-metrics) ![](https://mc.yandex.ru/watch/60763294)
Readable Markdown	Draw train and evaluation metrics in [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook) for two trained models. ## Method call format ``` compare(model, data=None, metrics=None, ntree_start=0, ntree_end=0, eval_period=1, thread_count=-1, tmp_dir=None, log_cout=sys.stdout, log_cerr=sys.stderr) ``` ## Parameters ### model #### Description The CatBoost model to compare with. Possible types CatBoost Model Default value Required parameter ### metrics #### Description The list of metrics to be calculated. [Supported metrics](https://catboost.ai/docs/en/references/custom-metric__supported-metrics) For example, if the AUC and Logloss metrics should be calculated, use the following construction: ``` ['Logloss', 'AUC'] ``` Possible types list of strings Default value Required parameter ### data #### Description A file or matrix with the input dataset, on which the compared metric values should be calculated. Possible types catboost.Pool Default value Required parameter ### ntree\_start #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to k to calculate metrics on every k\-th iteration. This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based. Possible types int Default value 0 ### ntree\_end #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to k to calculate metrics on every k\-th iteration. This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based. Possible types int Default value 0 (the index of the last tree to use equals to the number of trees in the model minus one) ### eval\_period #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to k to calculate metrics on every k\-th iteration. This parameter defines the step to iterate over the range `[`ntree\_start`;`ntree\_end`)`. For example, let's assume that the following parameter values are set: - `ntree_start` is set 0 - `ntree_end` is set to N (the total tree count) - `eval_period` is set to 2 In this case, the metrics are calculated for the following tree ranges: `[0, 2)`, `[0, 4)`, ... , `[0, N)` Possible types int Default value 1 (the trees are applied sequentially: the first tree, then the first two trees, etc.) ### 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) ### tmp\_dir #### Description The name of the temporary directory for intermediate results. Possible types String Default value None (the name is generated) ### log\_cout Output stream or callback for logging. Possible types - callable Python object - python object providing the `write()` method Default value sys.stdout ### log\_cerr Error stream or callback for logging. Possible types - callable Python object - python object providing the `write()` method Default value sys.stderr ## Examples ``` from catboost import Pool, CatBoostRegressor train_data = [[0, 3], [4, 1], [8, 1], [9, 1]] train_labels = [0, 0, 1, 1] eval_data = [[1, 3], [4, 2], [8, 2], [8, 3]] eval_labels = [1, 0, 0, 1] train_dataset = Pool(train_data, train_labels) eval_dataset = Pool(eval_data, eval_labels) model1 = CatBoostRegressor(iterations=100, learning_rate=0.1) model1.fit(train_dataset, verbose=False) model2 = CatBoostRegressor(iterations=100, learning_rate=0.3) model2.fit(train_dataset, verbose=False) model1.compare(model2, eval_dataset, ['RMSE']) ``` The following is a chart plotted with [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook) for the given example. ![](https://catboost.ai/docs/docs-assets/catboost/rev/176123d0b3d555dac6641baf853bbb288710bec5/en/images/interface__visualization-tools__jupyter__comparemodel__catboostregressor.png)
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