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Calculated Shard: 169 (from laksa061)

2. Crawled Status Check

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

Response:

{"found_url":"https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_utils_eval_metric","crawl_time":1775209280,"first_indexed_time":1732071543,"http_code":200,"src_unparsed":"ai,catboost!\/docs\/en\/concepts\/python-reference_utils_eval_metric s443","src_root_hash":"17435841955170310369","history_drop_reason":null,"meta_title":"eval_metric | CatBoost","meta_descriptions":["Calculate the specified metric on raw approximated values of the formula and label values. Method call format."],"attrs_boilerpipe_text":"Calculate the specified metric on raw approximated values of the formula and label values.\nMethod call format\neval_metric(label,\n            approx,\n            metric,\n            weight=\nNone\n,\n            group_id=\nNone\n,\n            subgroup_id=\nNone\n,\n            pairs=\nNone\n,\n            thread_count=-\n1\n)\nParameters\nlabel\nDescription\nA list of target variables (in other words, the label values of the objects).\nPossible types\nlist\nnumpy.ndarray\npandas.DataFrame\npandas.Series\npolars.Series\npolars.DataFrame\nDefault value\nRequired parameter\napprox\nDescription\nA list of approximate values for all input objects.\nPossible types\nlist\nnumpy.ndarray\npandas.DataFrame\npandas.Series\npolars.Series\npolars.DataFrame\nDefault value\nRequired parameter\nmetric\nDescription\nThe evaluation metric to calculate.\nSupported metrics\nRMSE\nLogloss\nMAE\nCrossEntropy\nQuantile\nLogLinQuantile\nLq\nMultiRMSE\nMultiClass\nMultiClassOneVsAll\nMultiLogloss\nMultiCrossEntropy\nMAPE\nPoisson\nPairLogit\nPairLogitPairwise\nQueryRMSE\nQuerySoftMax\nGroupQuantile\nTweedie\nSMAPE\nRecall\nPrecision\nF\nF1\nTotalF1\nAccuracy\nBalancedAccuracy\nBalancedErrorRate\nKappa\nWKappa\nLogLikelihoodOfPrediction\nAUC\nQueryAUC\nR2\nFairLoss\nNumErrors\nMCC\nBrierScore\nHingeLoss\nHammingLoss\nZeroOneLoss\nMSLE\nMedianAbsoluteError\nCox\nHuber\nExpectile\nMultiRMSE\nPairAccuracy\nQueryAverage\nPFound\nNDCG\nDCG\nFilteredDCG\nNormalizedGini\nPrecisionAt\nRecallAt\nMAP\nCtrFactor\nYetiRank\nYetiRankPairwise\nStochasticFilter\nStochasticRank\nLambdaMart\nPossible types\nstring\nDefault value\nRequired parameter\nweight\nDescription\nThe weights of objects.\nPossible types\nlist\nnumpy.ndarray\npandas.DataFrame\npandas.Series\npolars.Series\nDefault value\nNone\ngroup_id\nDescription\nGroup identifiers for all input objects. Supported identifier types are:\nint\nstring types (string or unicode for Python 2 and bytes or string for Python 3).\nPossible types\nlist\nnumpy.ndarray\npandas.DataFrame\npandas.Series\npolars.Series\nDefault value\nNone\nsubgroup_id\nDescription\nSubgroup identifiers for all input objects.\nPossible types\nlist\nnumpy.ndarray\npolars.Series\nDefault value\nNone\npairs\nDescription\nThe description is different for each group of possible types.\nPossible types\nlist, numpy.ndarray, pandas.DataFrame, polars.DataFrame\nThe pairs description in the form of a two-dimensional matrix of shape\nN\nby 2:\nN\nis the number of pairs.\nThe first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison.\nThe second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison.\nThis information is used for calculation and optimization of \nPairwise metrics\n.\nstring\nThe path to the input file that contains the \npairs description\n.\nThis information is used for calculation and optimization of \nPairwise metrics\n.\nDefault value\nNone\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)\nType of return value\nlist with metric values.\nUsage examples\nThe following is an example of usage with a\nregression metric\n:\nfrom\ncatboost.utils\nimport\neval_metric\n\nlabels = [\n0.2\n, -\n1\n,\n0.4\n]\npredictions = [\n0.4\n,\n0.1\n,\n0.9\n]\n\nrmse = eval_metric(labels, predictions,\n'RMSE'\n)\nThe following is an example of usage with a\nclassification metric\n:\nfrom\ncatboost.utils\nimport\neval_metric\nfrom\nmath\nimport\nlog\n\nlabels = [\n1\n,\n0\n,\n1\n]\nprobabilities = [\n0.4\n,\n0.1\n,\n0.9\n]\n# In binary classification it is necessary to apply the logit function\n# to the probabilities to get approxes.\nlogit =\nlambda\nx: log(x \/ (\n1\n- x))\napproxes =\nlist\n(\nmap\n(logit, probabilities))\n\naccuracy = eval_metric(labels, approxes,\n'Accuracy'\n)\nThe following is an example of usage with a\nranking metric\n:\nfrom\ncatboost.utils\nimport\neval_metric\n# The dataset consists of five objects. The first two belong to one group\n# and the other three to another.\ngroup_ids = [\n1\n,\n1\n,\n2\n,\n2\n,\n2\n]\n\nlabels = [\n0.9\n,\n0.1\n,\n0.5\n,\n0.4\n,\n0.8\n]\n# In ranking tasks it is not necessary to predict the same labels.\n# It is important to predict the right order of objects.\ngood_predictions = [\n0.5\n,\n0.4\n,\n0.2\n,\n0.1\n,\n0.3\n]\nbad_predictions = [\n0.4\n,\n0.5\n,\n0.2\n,\n0.3\n,\n0.1\n]\n\ngood_ndcg = eval_metric(labels, good_predictions,\n'NDCG'\n, group_id=group_ids)\nbad_ndcg = eval_metric(labels, bad_predictions,\n'NDCG'\n, group_id=group_ids)","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  - [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    - [Overview](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils)\n    - [create\\_cd](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_create_cd)\n    - [eval\\_metric](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric)\n    - [get\\_confusion\\_matrix](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_get_confusion_matrix)\n    - [get\\_gpu\\_device\\_count](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_get_gpu_device_count)\n    - [get\\_fnr\\_curve](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_get_fnr_curve)\n    - [get\\_fpr\\_curve](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_get_fpr_curve)\n    - [get\\_roc\\_curve](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_get_roc_curve)\n    - [quantize](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_quantize)\n    - [select\\_threshold](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_select_threshold)\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\neval\\_metric\n\n## In this article:\n- [Method call format](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#call-format)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#parameters)\n- [label](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#label)\n- [approx](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#approx)\n- [metric](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#metric)\n- [weight](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#weight)\n- [group\\_id](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#group_id)\n- [subgroup\\_id](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#subgroup_id)\n- [pairs](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#pairs)\n- [thread\\_count](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#thread_count)\n- [Type of return value](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#output-format)\n- [Usage examples](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#usage-examples)\n\n1. Python package\n2. [utils](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils)\n3. eval\\_metric\n\n# eval\\_metric\n- [Method call format](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#call-format)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#parameters)\n  - [label](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#label)\n  - [approx](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#approx)\n  - [metric](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#metric)\n  - [weight](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#weight)\n  - [group\\_id](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#group_id)\n  - [subgroup\\_id](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#subgroup_id)\n  - [pairs](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#pairs)\n  - [thread\\_count](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#thread_count)\n- [Type of return value](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#output-format)\n- [Usage examples](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_eval_metric#usage-examples)\n\nCalculate the specified metric on raw approximated values of the formula and label values.\n\n## Method call format\n```\neval_metric(label,\n            approx,\n            metric,\n            weight=None,\n            group_id=None,\n            subgroup_id=None,\n            pairs=None,\n            thread_count=-1)\n```\n## Parameters\n### label\n#### Description\nA list of target variables (in other words, the label values of the objects).\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\nRequired parameter\n\n### approx\n#### Description\nA list of approximate values for all input objects.\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\nRequired parameter\n\n### metric\n#### Description\nThe evaluation metric to calculate.\n\nSupported metrics\n\n- RMSE\n- Logloss\n- MAE\n- CrossEntropy\n- Quantile\n- LogLinQuantile\n- Lq\n- MultiRMSE\n- MultiClass\n- MultiClassOneVsAll\n- MultiLogloss\n- MultiCrossEntropy\n- MAPE\n- Poisson\n- PairLogit\n- PairLogitPairwise\n- QueryRMSE\n- QuerySoftMax\n- GroupQuantile\n- Tweedie\n- SMAPE\n- Recall\n- Precision\n- F\n- F1\n- TotalF1\n- Accuracy\n- BalancedAccuracy\n- BalancedErrorRate\n- Kappa\n- WKappa\n- LogLikelihoodOfPrediction\n- AUC\n- QueryAUC\n- R2\n- FairLoss\n- NumErrors\n- MCC\n- BrierScore\n- HingeLoss\n- HammingLoss\n- ZeroOneLoss\n- MSLE\n- MedianAbsoluteError\n- Cox\n- Huber\n- Expectile\n- MultiRMSE\n- PairAccuracy\n- QueryAverage\n- PFound\n- NDCG\n- DCG\n- FilteredDCG\n- NormalizedGini\n- PrecisionAt\n- RecallAt\n- MAP\n- CtrFactor\n- YetiRank\n- YetiRankPairwise\n- StochasticFilter\n- StochasticRank\n- LambdaMart\n\n**Possible types**\n\nstring\n\n**Default value**\n\nRequired parameter\n\n### weight\n#### Description\nThe weights of objects.\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\n**Default value**\n\nNone\n\n### group\\_id\n#### Description\nGroup identifiers for all input objects. Supported identifier types are:\n\n- int\n- string types (string or unicode for Python 2 and bytes or string for Python 3).\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\n**Default value**\n\nNone\n\n### subgroup\\_id\n#### Description\nSubgroup identifiers for all input objects.\n\n**Possible types**\n\n- list\n- numpy.ndarray\n- [polars.Series](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/series\/index.html)\n\n**Default value**\n\nNone\n\n### pairs\n#### Description\nThe description is different for each group of possible types.\n\n**Possible types**\n\nlist, numpy.ndarray, pandas.DataFrame, polars.DataFrame\n\nThe pairs description in the form of a two-dimensional matrix of shape `N` by 2:\n\n- `N` is the number of pairs.\n- The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison.\n- The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison.\n\nThis information is used for calculation and optimization of [Pairwise metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/loss-functions-ranking).\n\nstring\n\nThe path to the input file that contains the [pairs description](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/input-data_pairs-description).\n\nThis information is used for calculation and optimization of [Pairwise metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/loss-functions-ranking).\n\n**Default value**\n\nNone\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## Type of return value\nlist with metric values.\n\n## Usage examples\nThe following is an example of usage with a [regression metric](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/loss-functions-regression):\n```\nfrom catboost.utils import eval_metric\n\nlabels = [0.2, -1, 0.4]\npredictions = [0.4, 0.1, 0.9]\n\nrmse = eval_metric(labels, predictions, 'RMSE')\n```\nThe following is an example of usage with a [classification metric](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/loss-functions-classification):\n```\nfrom catboost.utils import eval_metric\nfrom math import log\n\nlabels = [1, 0, 1]\nprobabilities = [0.4, 0.1, 0.9]\n\n# In binary classification it is necessary to apply the logit function\n# to the probabilities to get approxes.\n\nlogit = lambda x: log(x \/ (1 - x))\napproxes = list(map(logit, probabilities))\n\naccuracy = eval_metric(labels, approxes, 'Accuracy')\n```\nThe following is an example of usage with a [ranking metric](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/loss-functions-ranking):\n```\nfrom catboost.utils import eval_metric\n\n# The dataset consists of five objects. The first two belong to one group\n# and the other three to another.\ngroup_ids = [1, 1, 2, 2, 2]\n\nlabels = [0.9, 0.1, 0.5, 0.4, 0.8]\n\n# In ranking tasks it is not necessary to predict the same labels.\n# It is important to predict the right order of objects.\ngood_predictions = [0.5, 0.4, 0.2, 0.1, 0.3]\nbad_predictions = [0.4, 0.5, 0.2, 0.3, 0.1]\n\ngood_ndcg = eval_metric(labels, good_predictions, 'NDCG', group_id=group_ids)\nbad_ndcg = eval_metric(labels, bad_predictions, 'NDCG', group_id=group_ids)\n```\n\n### Was the article helpful?\nYes\n\nNo\n\nPrevious\n\n[create\\_cd](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_create_cd)\n\nNext\n\n[get\\_confusion\\_matrix](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_utils_get_confusion_matrix)\n\n![](https:\/\/mc.yandex.ru\/watch\/60763294)","attrs_readable_markdown":"Calculate the specified metric on raw approximated values of the formula and label values.\n\n## Method call format\n```\neval_metric(label,\n            approx,\n            metric,\n            weight=None,\n            group_id=None,\n            subgroup_id=None,\n            pairs=None,\n            thread_count=-1)\n```\n## Parameters\n### label\n#### Description\nA list of target variables (in other words, the label values of the objects).\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\nRequired parameter\n\n### approx\n#### Description\nA list of approximate values for all input objects.\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\nRequired parameter\n\n### metric\n#### Description\nThe evaluation metric to calculate.\n\nSupported metrics\n\n- RMSE\n- Logloss\n- MAE\n- CrossEntropy\n- Quantile\n- LogLinQuantile\n- Lq\n- MultiRMSE\n- MultiClass\n- MultiClassOneVsAll\n- MultiLogloss\n- MultiCrossEntropy\n- MAPE\n- Poisson\n- PairLogit\n- PairLogitPairwise\n- QueryRMSE\n- QuerySoftMax\n- GroupQuantile\n- Tweedie\n- SMAPE\n- Recall\n- Precision\n- F\n- F1\n- TotalF1\n- Accuracy\n- BalancedAccuracy\n- BalancedErrorRate\n- Kappa\n- WKappa\n- LogLikelihoodOfPrediction\n- AUC\n- QueryAUC\n- R2\n- FairLoss\n- NumErrors\n- MCC\n- BrierScore\n- HingeLoss\n- HammingLoss\n- ZeroOneLoss\n- MSLE\n- MedianAbsoluteError\n- Cox\n- Huber\n- Expectile\n- MultiRMSE\n- PairAccuracy\n- QueryAverage\n- PFound\n- NDCG\n- DCG\n- FilteredDCG\n- NormalizedGini\n- PrecisionAt\n- RecallAt\n- MAP\n- CtrFactor\n- YetiRank\n- YetiRankPairwise\n- StochasticFilter\n- StochasticRank\n- LambdaMart\n\n**Possible types**\n\nstring\n\n**Default value**\n\nRequired parameter\n\n### weight\n#### Description\nThe weights of objects.\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\n**Default value**\n\nNone\n\n### group\\_id\n#### Description\nGroup identifiers for all input objects. Supported identifier types are:\n\n- int\n- string types (string or unicode for Python 2 and bytes or string for Python 3).\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\n**Default value**\n\nNone\n\n### subgroup\\_id\n#### Description\nSubgroup identifiers for all input objects.\n\n**Possible types**\n\n- list\n- numpy.ndarray\n- [polars.Series](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/series\/index.html)\n\n**Default value**\n\nNone\n\n### pairs\n#### Description\nThe description is different for each group of possible types.\n\n**Possible types**\n\nlist, numpy.ndarray, pandas.DataFrame, polars.DataFrame\n\nThe pairs description in the form of a two-dimensional matrix of shape `N` by 2:\n\n- `N` is the number of pairs.\n- The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison.\n- The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison.\n\nThis information is used for calculation and optimization of [Pairwise metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/loss-functions-ranking).\n\nstring\n\nThe path to the input file that contains the [pairs description](https:\/\/catboost.ai\/docs\/en\/concepts\/input-data_pairs-description).\n\nThis information is used for calculation and optimization of [Pairwise metrics](https:\/\/catboost.ai\/docs\/en\/concepts\/loss-functions-ranking).\n\n**Default value**\n\nNone\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## Type of return value\nlist with metric values.\n\n## Usage examples\nThe following is an example of usage with a [regression metric](https:\/\/catboost.ai\/docs\/en\/concepts\/loss-functions-regression):\n```\nfrom catboost.utils import eval_metric\n\nlabels = [0.2, -1, 0.4]\npredictions = [0.4, 0.1, 0.9]\n\nrmse = eval_metric(labels, predictions, 'RMSE')\n```\nThe following is an example of usage with a [classification metric](https:\/\/catboost.ai\/docs\/en\/concepts\/loss-functions-classification):\n```\nfrom catboost.utils import eval_metric\nfrom math import log\n\nlabels = [1, 0, 1]\nprobabilities = [0.4, 0.1, 0.9]\n\n# In binary classification it is necessary to apply the logit function\n# to the probabilities to get approxes.\n\nlogit = lambda x: log(x \/ (1 - x))\napproxes = list(map(logit, probabilities))\n\naccuracy = eval_metric(labels, approxes, 'Accuracy')\n```\nThe following is an example of usage with a [ranking metric](https:\/\/catboost.ai\/docs\/en\/concepts\/loss-functions-ranking):\n```\nfrom catboost.utils import eval_metric\n\n# The dataset consists of five objects. The first two belong to one group\n# and the other three to another.\ngroup_ids = [1, 1, 2, 2, 2]\n\nlabels = [0.9, 0.1, 0.5, 0.4, 0.8]\n\n# In ranking tasks it is not necessary to predict the same labels.\n# It is important to predict the right order of objects.\ngood_predictions = [0.5, 0.4, 0.2, 0.1, 0.3]\nbad_predictions = [0.4, 0.5, 0.2, 0.3, 0.1]\n\ngood_ndcg = eval_metric(labels, good_predictions, 'NDCG', group_id=group_ids)\nbad_ndcg = eval_metric(labels, bad_predictions, 'NDCG', group_id=group_ids)\n```","meta_canonical":null}

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Meta Description	Calculate the specified metric on raw approximated values of the formula and label values. Method call format.
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Boilerpipe Text	Calculate the specified metric on raw approximated values of the formula and label values. Method call format eval_metric(label, approx, metric, weight= None , group_id= None , subgroup_id= None , pairs= None , thread_count=- 1 ) Parameters label Description A list of target variables (in other words, the label values of the objects). Possible types list numpy.ndarray pandas.DataFrame pandas.Series polars.Series polars.DataFrame Default value Required parameter approx Description A list of approximate values for all input objects. Possible types list numpy.ndarray pandas.DataFrame pandas.Series polars.Series polars.DataFrame Default value Required parameter metric Description The evaluation metric to calculate. Supported metrics RMSE Logloss MAE CrossEntropy Quantile LogLinQuantile Lq MultiRMSE MultiClass MultiClassOneVsAll MultiLogloss MultiCrossEntropy MAPE Poisson PairLogit PairLogitPairwise QueryRMSE QuerySoftMax GroupQuantile Tweedie SMAPE Recall Precision F F1 TotalF1 Accuracy BalancedAccuracy BalancedErrorRate Kappa WKappa LogLikelihoodOfPrediction AUC QueryAUC R2 FairLoss NumErrors MCC BrierScore HingeLoss HammingLoss ZeroOneLoss MSLE MedianAbsoluteError Cox Huber Expectile MultiRMSE PairAccuracy QueryAverage PFound NDCG DCG FilteredDCG NormalizedGini PrecisionAt RecallAt MAP CtrFactor YetiRank YetiRankPairwise StochasticFilter StochasticRank LambdaMart Possible types string Default value Required parameter weight Description The weights of objects. Possible types list numpy.ndarray pandas.DataFrame pandas.Series polars.Series Default value None group_id Description Group identifiers for all input objects. Supported identifier types are: int string types (string or unicode for Python 2 and bytes or string for Python 3). Possible types list numpy.ndarray pandas.DataFrame pandas.Series polars.Series Default value None subgroup_id Description Subgroup identifiers for all input objects. Possible types list numpy.ndarray polars.Series Default value None pairs Description The description is different for each group of possible types. Possible types list, numpy.ndarray, pandas.DataFrame, polars.DataFrame The pairs description in the form of a two-dimensional matrix of shape N by 2: N is the number of pairs. The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison. The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison. This information is used for calculation and optimization of Pairwise metrics . string The path to the input file that contains the pairs description . This information is used for calculation and optimization of Pairwise metrics . Default value None thread_count Description The number of threads to use. Optimizes the speed of execution. This parameter doesn't affect results. Possible types int Default value -1 (the number of threads is equal to the number of processor cores) Type of return value list with metric values. Usage examples The following is an example of usage with a regression metric : from catboost.utils import eval_metric labels = [ 0.2 , - 1 , 0.4 ] predictions = [ 0.4 , 0.1 , 0.9 ] rmse = eval_metric(labels, predictions, 'RMSE' ) The following is an example of usage with a classification metric : from catboost.utils import eval_metric from math import log labels = [ 1 , 0 , 1 ] probabilities = [ 0.4 , 0.1 , 0.9 ] # In binary classification it is necessary to apply the logit function # to the probabilities to get approxes. logit = lambda x: log(x / ( 1 - x)) approxes = list ( map (logit, probabilities)) accuracy = eval_metric(labels, approxes, 'Accuracy' ) The following is an example of usage with a ranking metric : from catboost.utils import eval_metric # The dataset consists of five objects. The first two belong to one group # and the other three to another. group_ids = [ 1 , 1 , 2 , 2 , 2 ] labels = [ 0.9 , 0.1 , 0.5 , 0.4 , 0.8 ] # In ranking tasks it is not necessary to predict the same labels. # It is important to predict the right order of objects. good_predictions = [ 0.5 , 0.4 , 0.2 , 0.1 , 0.3 ] bad_predictions = [ 0.4 , 0.5 , 0.2 , 0.3 , 0.1 ] good_ndcg = eval_metric(labels, good_predictions, 'NDCG' , group_id=group_ids) bad_ndcg = eval_metric(labels, bad_predictions, 'NDCG' , group_id=group_ids)
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 - [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 - [Overview](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils) - [create\_cd](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_create_cd) - [eval\_metric](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric) - [get\_confusion\_matrix](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_get_confusion_matrix) - [get\_gpu\_device\_count](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_get_gpu_device_count) - [get\_fnr\_curve](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_get_fnr_curve) - [get\_fpr\_curve](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_get_fpr_curve) - [get\_roc\_curve](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_get_roc_curve) - [quantize](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_quantize) - [select\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_select_threshold) - [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) eval\_metric ## In this article: - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#parameters) - [label](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#label) - [approx](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#approx) - [metric](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#metric) - [weight](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#weight) - [group\_id](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#group_id) - [subgroup\_id](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#subgroup_id) - [pairs](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#pairs) - [thread\_count](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#thread_count) - [Type of return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#output-format) - [Usage examples](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#usage-examples) 1. Python package 2. [utils](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils) 3. eval\_metric # eval\_metric - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#parameters) - [label](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#label) - [approx](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#approx) - [metric](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#metric) - [weight](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#weight) - [group\_id](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#group_id) - [subgroup\_id](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#subgroup_id) - [pairs](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#pairs) - [thread\_count](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#thread_count) - [Type of return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#output-format) - [Usage examples](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_eval_metric#usage-examples) Calculate the specified metric on raw approximated values of the formula and label values. ## Method call format ``` eval_metric(label, approx, metric, weight=None, group_id=None, subgroup_id=None, pairs=None, thread_count=-1) ``` ## Parameters ### label #### Description A list of target variables (in other words, the label values of the objects). 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 Required parameter ### approx #### Description A list of approximate values for all input objects. 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 Required parameter ### metric #### Description The evaluation metric to calculate. Supported metrics - RMSE - Logloss - MAE - CrossEntropy - Quantile - LogLinQuantile - Lq - MultiRMSE - MultiClass - MultiClassOneVsAll - MultiLogloss - MultiCrossEntropy - MAPE - Poisson - PairLogit - PairLogitPairwise - QueryRMSE - QuerySoftMax - GroupQuantile - Tweedie - SMAPE - Recall - Precision - F - F1 - TotalF1 - Accuracy - BalancedAccuracy - BalancedErrorRate - Kappa - WKappa - LogLikelihoodOfPrediction - AUC - QueryAUC - R2 - FairLoss - NumErrors - MCC - BrierScore - HingeLoss - HammingLoss - ZeroOneLoss - MSLE - MedianAbsoluteError - Cox - Huber - Expectile - MultiRMSE - PairAccuracy - QueryAverage - PFound - NDCG - DCG - FilteredDCG - NormalizedGini - PrecisionAt - RecallAt - MAP - CtrFactor - YetiRank - YetiRankPairwise - StochasticFilter - StochasticRank - LambdaMart Possible types string Default value Required parameter ### weight #### Description The weights of objects. Possible types - list - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) Default value None ### group\_id #### Description Group identifiers for all input objects. Supported identifier types are: - int - string types (string or unicode for Python 2 and bytes or string for Python 3). Possible types - list - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) Default value None ### subgroup\_id #### Description Subgroup identifiers for all input objects. Possible types - list - numpy.ndarray - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) Default value None ### pairs #### Description The description is different for each group of possible types. Possible types list, numpy.ndarray, pandas.DataFrame, polars.DataFrame The pairs description in the form of a two-dimensional matrix of shape `N` by 2: - `N` is the number of pairs. - The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison. - The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison. This information is used for calculation and optimization of [Pairwise metrics](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions-ranking). string The path to the input file that contains the [pairs description](https://catboost.ai/docs/en/concepts/en/concepts/input-data_pairs-description). This information is used for calculation and optimization of [Pairwise metrics](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions-ranking). Default value None ### thread\_count #### Description The number of threads to use. Optimizes the speed of execution. This parameter doesn't affect results. Possible types int Default value \-1 (the number of threads is equal to the number of processor cores) ## Type of return value list with metric values. ## Usage examples The following is an example of usage with a [regression metric](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions-regression): ``` from catboost.utils import eval_metric labels = [0.2, -1, 0.4] predictions = [0.4, 0.1, 0.9] rmse = eval_metric(labels, predictions, 'RMSE') ``` The following is an example of usage with a [classification metric](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions-classification): ``` from catboost.utils import eval_metric from math import log labels = [1, 0, 1] probabilities = [0.4, 0.1, 0.9] # In binary classification it is necessary to apply the logit function # to the probabilities to get approxes. logit = lambda x: log(x / (1 - x)) approxes = list(map(logit, probabilities)) accuracy = eval_metric(labels, approxes, 'Accuracy') ``` The following is an example of usage with a [ranking metric](https://catboost.ai/docs/en/concepts/en/concepts/loss-functions-ranking): ``` from catboost.utils import eval_metric # The dataset consists of five objects. The first two belong to one group # and the other three to another. group_ids = [1, 1, 2, 2, 2] labels = [0.9, 0.1, 0.5, 0.4, 0.8] # In ranking tasks it is not necessary to predict the same labels. # It is important to predict the right order of objects. good_predictions = [0.5, 0.4, 0.2, 0.1, 0.3] bad_predictions = [0.4, 0.5, 0.2, 0.3, 0.1] good_ndcg = eval_metric(labels, good_predictions, 'NDCG', group_id=group_ids) bad_ndcg = eval_metric(labels, bad_predictions, 'NDCG', group_id=group_ids) ``` ### Was the article helpful? Yes No Previous [create\_cd](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_create_cd) Next [get\_confusion\_matrix](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_utils_get_confusion_matrix) ![](https://mc.yandex.ru/watch/60763294)
Readable Markdown	Calculate the specified metric on raw approximated values of the formula and label values. ## Method call format ``` eval_metric(label, approx, metric, weight=None, group_id=None, subgroup_id=None, pairs=None, thread_count=-1) ``` ## Parameters ### label #### Description A list of target variables (in other words, the label values of the objects). 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 Required parameter ### approx #### Description A list of approximate values for all input objects. 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 Required parameter ### metric #### Description The evaluation metric to calculate. Supported metrics - RMSE - Logloss - MAE - CrossEntropy - Quantile - LogLinQuantile - Lq - MultiRMSE - MultiClass - MultiClassOneVsAll - MultiLogloss - MultiCrossEntropy - MAPE - Poisson - PairLogit - PairLogitPairwise - QueryRMSE - QuerySoftMax - GroupQuantile - Tweedie - SMAPE - Recall - Precision - F - F1 - TotalF1 - Accuracy - BalancedAccuracy - BalancedErrorRate - Kappa - WKappa - LogLikelihoodOfPrediction - AUC - QueryAUC - R2 - FairLoss - NumErrors - MCC - BrierScore - HingeLoss - HammingLoss - ZeroOneLoss - MSLE - MedianAbsoluteError - Cox - Huber - Expectile - MultiRMSE - PairAccuracy - QueryAverage - PFound - NDCG - DCG - FilteredDCG - NormalizedGini - PrecisionAt - RecallAt - MAP - CtrFactor - YetiRank - YetiRankPairwise - StochasticFilter - StochasticRank - LambdaMart Possible types string Default value Required parameter ### weight #### Description The weights of objects. Possible types - list - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) Default value None ### group\_id #### Description Group identifiers for all input objects. Supported identifier types are: - int - string types (string or unicode for Python 2 and bytes or string for Python 3). Possible types - list - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) Default value None ### subgroup\_id #### Description Subgroup identifiers for all input objects. Possible types - list - numpy.ndarray - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) Default value None ### pairs #### Description The description is different for each group of possible types. Possible types list, numpy.ndarray, pandas.DataFrame, polars.DataFrame The pairs description in the form of a two-dimensional matrix of shape `N` by 2: - `N` is the number of pairs. - The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison. - The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison. This information is used for calculation and optimization of [Pairwise metrics](https://catboost.ai/docs/en/concepts/loss-functions-ranking). string The path to the input file that contains the [pairs description](https://catboost.ai/docs/en/concepts/input-data_pairs-description). This information is used for calculation and optimization of [Pairwise metrics](https://catboost.ai/docs/en/concepts/loss-functions-ranking). Default value None ### thread\_count #### Description The number of threads to use. Optimizes the speed of execution. This parameter doesn't affect results. Possible types int Default value \-1 (the number of threads is equal to the number of processor cores) ## Type of return value list with metric values. ## Usage examples The following is an example of usage with a [regression metric](https://catboost.ai/docs/en/concepts/loss-functions-regression): ``` from catboost.utils import eval_metric labels = [0.2, -1, 0.4] predictions = [0.4, 0.1, 0.9] rmse = eval_metric(labels, predictions, 'RMSE') ``` The following is an example of usage with a [classification metric](https://catboost.ai/docs/en/concepts/loss-functions-classification): ``` from catboost.utils import eval_metric from math import log labels = [1, 0, 1] probabilities = [0.4, 0.1, 0.9] # In binary classification it is necessary to apply the logit function # to the probabilities to get approxes. logit = lambda x: log(x / (1 - x)) approxes = list(map(logit, probabilities)) accuracy = eval_metric(labels, approxes, 'Accuracy') ``` The following is an example of usage with a [ranking metric](https://catboost.ai/docs/en/concepts/loss-functions-ranking): ``` from catboost.utils import eval_metric # The dataset consists of five objects. The first two belong to one group # and the other three to another. group_ids = [1, 1, 2, 2, 2] labels = [0.9, 0.1, 0.5, 0.4, 0.8] # In ranking tasks it is not necessary to predict the same labels. # It is important to predict the right order of objects. good_predictions = [0.5, 0.4, 0.2, 0.1, 0.3] bad_predictions = [0.4, 0.5, 0.2, 0.3, 0.1] good_ndcg = eval_metric(labels, good_predictions, 'NDCG', group_id=group_ids) bad_ndcg = eval_metric(labels, bad_predictions, 'NDCG', group_id=group_ids) ```
Shard	169 (laksa)
Root Hash	17435841955170310369
Unparsed URL	ai,catboost!/docs/en/concepts/python-reference_utils_eval_metric s443