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

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{"found_url":"https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostregressor_staged_predict","crawl_time":1775656482,"first_indexed_time":1731946768,"http_code":200,"src_unparsed":"ai,catboost!\/docs\/en\/concepts\/python-reference_catboostregressor_staged_predict s443","src_root_hash":"17435841955170310369","history_drop_reason":null,"meta_title":"staged_predict | CatBoost","meta_descriptions":["Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i). Note."],"attrs_boilerpipe_text":"Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i).\nNote\nThe model prediction results will be correct only if the\ndata\nparameter with feature values contains all the features used in the model. Typically, the order of these features must match the order of the corresponding columns that is provided during the training. But if feature names are provided both during the training and when applying the model, they can be matched by names instead of columns order. Feature names can be specified if the\ndata\nparameter has one of the following types:\nFeaturesData\ncatboost.Pool\npandas.DataFrame\n(in this case, feature names are taken from column names)\npolars.DataFrame\n(in this case, feature names are taken from column names)\nMethod call format\nstaged_predict(data,\n               prediction_type=\nNone\n,\n               ntree_start=\n0\n,\n               ntree_end=\n0\n,\n               eval_period=\n1\n,\n               thread_count=-\n1\n,\n               verbose=\nFalse\n)\nParameters\ndata\nDescription\nFeature values data.\nThe format depends on the number of input objects:\nMultiple — Matrix-like data of shape\n(object_count, feature_count)\nSingle — An array\nPossible types\nFor multiple objects:\ncatboost.Pool\nlist of lists\nnumpy.ndarray of shape\n(object_count, feature_count)\npandas.DataFrame\npandas.SparseDataFrame\npandas.Series\npolars.DataFrame\ncatboost.FeaturesData\nscipy.sparse.spmatrix (all subclasses except dia_matrix)\nFor a single object:\nlist of feature values\none-dimensional numpy.ndarray with feature values\nDefault value\nRequired parameter\nprediction_type\nDescription\nThe required prediction type.\nSupported prediction types:\nProbability\nClass\nRawFormulaVal\nExponent\nLogProbability\nPossible types\nstring\nDefault value\nNone (Exponent for Poisson and Tweedie, RawFormulaVal for all other loss functions)\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)\nverbose\nDescription\nOutput the measured evaluation metric to stderr.\nPossible types\nbool\nDefault value\nNone\nReturn value\nGenerator that produces predictions with a sequentially growing subset of trees from the model. The type of generated values depends on the number of input objects:\nSingle object — Single float formula return value\nMultiple objects — One-dimensional numpy.ndarray of formula values for each object.","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\nstaged\\_predict\n\n## In this article:\n- [Method call format](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#call-format)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#parameters)\n- [data](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#data)\n- [prediction\\_type](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#prediction_type)\n- [ntree\\_start](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#ntree_start)\n- [ntree\\_end](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#ntree_end)\n- [eval\\_period](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#eval_period)\n- [thread\\_count](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#thread_count)\n- [verbose](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#verbose)\n- [Return value](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#output-format)\n\n1. Python package\n2. [CatBoostRegressor](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor)\n3. staged\\_predict\n\n# staged\\_predict\n- [Method call format](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#call-format)\n- [Parameters](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#parameters)\n  - [data](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#data)\n  - [prediction\\_type](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#prediction_type)\n  - [ntree\\_start](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#ntree_start)\n  - [ntree\\_end](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#ntree_end)\n  - [eval\\_period](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#eval_period)\n  - [thread\\_count](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#thread_count)\n  - [verbose](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#verbose)\n- [Return value](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_staged_predict#output-format)\n\nApply the model to the given dataset and calculate the results taking into consideration only the trees in the range \\[0; i).\n\nNote\n\nThe model prediction results will be correct only if the `data` parameter with feature values contains all the features used in the model. Typically, the order of these features must match the order of the corresponding columns that is provided during the training. But if feature names are provided both during the training and when applying the model, they can be matched by names instead of columns order. Feature names can be specified if the `data` parameter has one of the following types:\n\n- [FeaturesData](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-features-data__desc)\n- [catboost.Pool](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_pool)\n- [pandas.DataFrame](https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/frame.html) (in this case, feature names are taken from column names)\n- [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html) (in this case, feature names are taken from column names)\n## Method call format\n```\nstaged_predict(data,\n               prediction_type=None,\n               ntree_start=0,\n               ntree_end=0,\n               eval_period=1,\n               thread_count=-1,\n               verbose=False)\n```\n## Parameters\n### data\n#### Description\nFeature values data.\n\nThe format depends on the number of input objects:\n\n- Multiple — Matrix-like data of shape `(object_count, feature_count)`\n- Single — An array\n\n**Possible types**\n\nFor multiple objects:\n\n- catboost.Pool\n- list of lists\n- numpy.ndarray of shape `(object_count, feature_count)`\n- pandas.DataFrame\n- pandas.SparseDataFrame\n- pandas.Series\n- [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html)\n- [catboost.FeaturesData](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-features-data__desc)\n- scipy.sparse.spmatrix (all subclasses except dia\\_matrix)\n\nFor a single object:\n\n- list of feature values\n- one-dimensional numpy.ndarray with feature values\n\n**Default value**\n\nRequired parameter\n\n### prediction\\_type\n#### Description\nThe required prediction type.\n\nSupported prediction types:\n\n- Probability\n- Class\n- RawFormulaVal\n- Exponent\n- LogProbability\n\n**Possible types**\n\nstring\n\n**Default value**\n\nNone (Exponent for Poisson and Tweedie, RawFormulaVal for all other loss functions)\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### verbose\n#### Description\nOutput the measured evaluation metric to stderr.\n\n**Possible types**\n\nbool\n\n**Default value**\n\nNone\n\n## Return value\nGenerator that produces predictions with a sequentially growing subset of trees from the model. The type of generated values depends on the number of input objects:\n\n- Single object — Single float formula return value\n- Multiple objects — One-dimensional numpy.ndarray of formula values for each object.\n\n### Was the article helpful?\nYes\n\nNo\n\nPrevious\n\n[shrink](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_catboostregressor_shrink)\n\nNext\n\n[cv](https:\/\/catboost.ai\/docs\/en\/concepts\/en\/concepts\/python-reference_cv)\n\n![](https:\/\/mc.yandex.ru\/watch\/60763294)","attrs_readable_markdown":"Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range \\[0; i).\n\nNote\n\nThe model prediction results will be correct only if the `data` parameter with feature values contains all the features used in the model. Typically, the order of these features must match the order of the corresponding columns that is provided during the training. But if feature names are provided both during the training and when applying the model, they can be matched by names instead of columns order. Feature names can be specified if the `data` parameter has one of the following types:\n\n- [FeaturesData](https:\/\/catboost.ai\/docs\/en\/concepts\/python-features-data__desc)\n- [catboost.Pool](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_pool)\n- [pandas.DataFrame](https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/frame.html) (in this case, feature names are taken from column names)\n- [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html) (in this case, feature names are taken from column names)\n## Method call format\n```\nstaged_predict(data,\n               prediction_type=None,\n               ntree_start=0,\n               ntree_end=0,\n               eval_period=1,\n               thread_count=-1,\n               verbose=False)\n```\n## Parameters\n### data\n#### Description\nFeature values data.\n\nThe format depends on the number of input objects:\n\n- Multiple — Matrix-like data of shape `(object_count, feature_count)`\n- Single — An array\n\n**Possible types**\n\nFor multiple objects:\n\n- catboost.Pool\n- list of lists\n- numpy.ndarray of shape `(object_count, feature_count)`\n- pandas.DataFrame\n- pandas.SparseDataFrame\n- pandas.Series\n- [polars.DataFrame](https:\/\/docs.pola.rs\/api\/python\/stable\/reference\/dataframe\/index.html)\n- [catboost.FeaturesData](https:\/\/catboost.ai\/docs\/en\/concepts\/python-features-data__desc)\n- scipy.sparse.spmatrix (all subclasses except dia\\_matrix)\n\nFor a single object:\n\n- list of feature values\n- one-dimensional numpy.ndarray with feature values\n\n**Default value**\n\nRequired parameter\n\n### prediction\\_type\n#### Description\nThe required prediction type.\n\nSupported prediction types:\n\n- Probability\n- Class\n- RawFormulaVal\n- Exponent\n- LogProbability\n\n**Possible types**\n\nstring\n\n**Default value**\n\nNone (Exponent for Poisson and Tweedie, RawFormulaVal for all other loss functions)\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### verbose\n#### Description\nOutput the measured evaluation metric to stderr.\n\n**Possible types**\n\nbool\n\n**Default value**\n\nNone\n\n## Return value\nGenerator that produces predictions with a sequentially growing subset of trees from the model. The type of generated values depends on the number of input objects:\n\n- Single object — Single float formula return value\n- Multiple objects — One-dimensional numpy.ndarray of formula values for each object.","meta_canonical":null}

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URL	https://catboost.ai/docs/en/concepts/python-reference_catboostregressor_staged_predict
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Meta Title	staged_predict \| CatBoost
Meta Description	Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i). Note.
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Boilerpipe Text	Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i). Note The model prediction results will be correct only if the data parameter with feature values contains all the features used in the model. Typically, the order of these features must match the order of the corresponding columns that is provided during the training. But if feature names are provided both during the training and when applying the model, they can be matched by names instead of columns order. Feature names can be specified if the data parameter has one of the following types: FeaturesData catboost.Pool pandas.DataFrame (in this case, feature names are taken from column names) polars.DataFrame (in this case, feature names are taken from column names) Method call format staged_predict(data, prediction_type= None , ntree_start= 0 , ntree_end= 0 , eval_period= 1 , thread_count=- 1 , verbose= False ) Parameters data Description Feature values data. The format depends on the number of input objects: Multiple — Matrix-like data of shape (object_count, feature_count) Single — An array Possible types For multiple objects: catboost.Pool list of lists numpy.ndarray of shape (object_count, feature_count) pandas.DataFrame pandas.SparseDataFrame pandas.Series polars.DataFrame catboost.FeaturesData scipy.sparse.spmatrix (all subclasses except dia_matrix) For a single object: list of feature values one-dimensional numpy.ndarray with feature values Default value Required parameter prediction_type Description The required prediction type. Supported prediction types: Probability Class RawFormulaVal Exponent LogProbability Possible types string Default value None (Exponent for Poisson and Tweedie, RawFormulaVal for all other loss functions) 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) verbose Description Output the measured evaluation metric to stderr. Possible types bool Default value None Return value Generator that produces predictions with a sequentially growing subset of trees from the model. The type of generated values depends on the number of input objects: Single object — Single float formula return value Multiple objects — One-dimensional numpy.ndarray of formula values for each object.
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) staged\_predict ## In this article: - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#parameters) - [data](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#data) - [prediction\_type](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#prediction_type) - [ntree\_start](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#ntree_start) - [ntree\_end](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#ntree_end) - [eval\_period](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#eval_period) - [thread\_count](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#thread_count) - [verbose](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#verbose) - [Return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#output-format) 1. Python package 2. [CatBoostRegressor](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor) 3. staged\_predict # staged\_predict - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#parameters) - [data](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#data) - [prediction\_type](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#prediction_type) - [ntree\_start](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#ntree_start) - [ntree\_end](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#ntree_end) - [eval\_period](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#eval_period) - [thread\_count](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#thread_count) - [verbose](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#verbose) - [Return value](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_staged_predict#output-format) Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range \[0; i). Note The model prediction results will be correct only if the `data` parameter with feature values contains all the features used in the model. Typically, the order of these features must match the order of the corresponding columns that is provided during the training. But if feature names are provided both during the training and when applying the model, they can be matched by names instead of columns order. Feature names can be specified if the `data` parameter has one of the following types: - [FeaturesData](https://catboost.ai/docs/en/concepts/en/concepts/python-features-data__desc) - [catboost.Pool](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_pool) - [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html) (in this case, feature names are taken from column names) - [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) (in this case, feature names are taken from column names) ## Method call format ``` staged_predict(data, prediction_type=None, ntree_start=0, ntree_end=0, eval_period=1, thread_count=-1, verbose=False) ``` ## Parameters ### data #### Description Feature values data. The format depends on the number of input objects: - Multiple — Matrix-like data of shape `(object_count, feature_count)` - Single — An array Possible types For multiple objects: - catboost.Pool - list of lists - numpy.ndarray of shape `(object_count, feature_count)` - pandas.DataFrame - pandas.SparseDataFrame - pandas.Series - [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) - [catboost.FeaturesData](https://catboost.ai/docs/en/concepts/en/concepts/python-features-data__desc) - scipy.sparse.spmatrix (all subclasses except dia\_matrix) For a single object: - list of feature values - one-dimensional numpy.ndarray with feature values Default value Required parameter ### prediction\_type #### Description The required prediction type. Supported prediction types: - Probability - Class - RawFormulaVal - Exponent - LogProbability Possible types string Default value None (Exponent for Poisson and Tweedie, RawFormulaVal for all other loss functions) ### 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) ### verbose #### Description Output the measured evaluation metric to stderr. Possible types bool Default value None ## Return value Generator that produces predictions with a sequentially growing subset of trees from the model. The type of generated values depends on the number of input objects: - Single object — Single float formula return value - Multiple objects — One-dimensional numpy.ndarray of formula values for each object. ### Was the article helpful? Yes No Previous [shrink](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostregressor_shrink) Next [cv](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_cv) ![](https://mc.yandex.ru/watch/60763294)
Readable Markdown	Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range \[0; i). Note The model prediction results will be correct only if the `data` parameter with feature values contains all the features used in the model. Typically, the order of these features must match the order of the corresponding columns that is provided during the training. But if feature names are provided both during the training and when applying the model, they can be matched by names instead of columns order. Feature names can be specified if the `data` parameter has one of the following types: - [FeaturesData](https://catboost.ai/docs/en/concepts/python-features-data__desc) - [catboost.Pool](https://catboost.ai/docs/en/concepts/python-reference_pool) - [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html) (in this case, feature names are taken from column names) - [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) (in this case, feature names are taken from column names) ## Method call format ``` staged_predict(data, prediction_type=None, ntree_start=0, ntree_end=0, eval_period=1, thread_count=-1, verbose=False) ``` ## Parameters ### data #### Description Feature values data. The format depends on the number of input objects: - Multiple — Matrix-like data of shape `(object_count, feature_count)` - Single — An array Possible types For multiple objects: - catboost.Pool - list of lists - numpy.ndarray of shape `(object_count, feature_count)` - pandas.DataFrame - pandas.SparseDataFrame - pandas.Series - [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) - [catboost.FeaturesData](https://catboost.ai/docs/en/concepts/python-features-data__desc) - scipy.sparse.spmatrix (all subclasses except dia\_matrix) For a single object: - list of feature values - one-dimensional numpy.ndarray with feature values Default value Required parameter ### prediction\_type #### Description The required prediction type. Supported prediction types: - Probability - Class - RawFormulaVal - Exponent - LogProbability Possible types string Default value None (Exponent for Poisson and Tweedie, RawFormulaVal for all other loss functions) ### 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) ### verbose #### Description Output the measured evaluation metric to stderr. Possible types bool Default value None ## Return value Generator that produces predictions with a sequentially growing subset of trees from the model. The type of generated values depends on the number of input objects: - Single object — Single float formula return value - Multiple objects — One-dimensional numpy.ndarray of formula values for each object.
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