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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==' \
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{"found_url":"https:\/\/catboost.ai\/docs\/en\/features\/feature-importances-calculation","crawl_time":1776333926,"first_indexed_time":1731601231,"http_code":200,"src_unparsed":"ai,catboost!\/docs\/en\/features\/feature-importances-calculation s443","src_root_hash":"17435841955170310369","history_drop_reason":null,"meta_title":"Feature importances | CatBoost","meta_descriptions":["CatBoost provides different types of feature importance calculation: Feature importance calculation type Implementations."],"attrs_boilerpipe_text":"CatBoost provides different types of feature importance calculation:\nFeature importance calculation type\nImplementations\nThe most important features in the formula\n-\nPredictionValuesChange\n-\nLossFunctionChange\n-\nInternalFeatureImportance\nThe contribution of each feature to the formula\nShapValues\nThe features that work well together\n-\nInteraction\n-\nInternalInteraction\nChoose the implementation for more details.\nPython package\nUse one of the following methods:\nUse the feature_importances_ attribute.\nUse one of the following methods to calculate the feature importances after model training:\nClass\nDescription\nCatBoost\nget_feature_importance\nCatBoostClassifier\nget_feature_importance\nCatBoostRegressor\nget_feature_importance\nThese methods calculate and return the \nfeature importances\n.\nR package\nUse one of the following methods:\nUse the \nfeature_importances\nattribute\nto get the feature importances.\nUse one of the following methods to calculate the feature importances after model training:\nMethod\nDescription\ncatboost.get_feature_importance\nCalculate the\nfeature importances\n(\nFeature importance\nand \nFeature interaction strength\n).\nCommand-line version\nUse the following command to calculate the feature importances during model training:\nCommand\nCommand keys\nKey description\ncatboost fit\n--fstr-file\nThe name of the resulting file that contains \nregular feature importance\ndata (see\nFeature importance\n).\nSet the required file name for further feature importance analysis.\n--fstr-internal-file\nThe name of the resulting file that contains \ninternal feature importance\ndata (see\nFeature importance\n).\nSet the required file name for further internal feature importance analysis.\nUse the following command to calculate the feature importances after model training:\nCommand\nPurpose\ncatboost fstr\nCalculate feature importances.\nModel analysis","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\/features\/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](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/training)\n  - [Training on GPU](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/training-on-gpu)\n  - [Regular prediction](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/prediction)\n  - [Staged prediction](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/staged-prediction)\n  - [Cross-validation](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/cross-validation)\n  - [Feature importances](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/feature-importances-calculation)\n  - [User-defined metrics](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/custom-loss-functions)\n  - [Using the overfitting detector](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/overfitting-detector-desc)\n  - [Export a model to CoreML](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/export-model-to-core-ml)\n  - [Pre-trained data](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/proceed-training)\n  - [Calculate metrics](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/eval-metrics)\n  - [Categorical features](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/categorical-features)\n  - [Text features](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/text-features)\n  - [Embeddings features](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/embeddings-features)\n  - [Aggregated graph features](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/graph-aggregated-features)\n  - [Implemented metrics](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/loss-functions-desc)\n  - [Export a model to Python or C++](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/export-model-to-python)\n  - [Export a model to JSON](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/export-model-to-json)\n  - [Object importances](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/object-importances-calcution)\n- Training parameters\n- Python package\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\/features\/en\/concepts\/parameter-tuning)\n- [Speeding up the training](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/speed-up-training)\n- Data visualization\n- Algorithm details\n- [FAQ](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/faq)\n- Educational materials\n- [Development and contributions](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/development-and-contributions)\n- [Contacts](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/contacts)\n\nFeature importances\n\n## In this article:\n- [Python package](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/feature-importances-calculation#python-package)\n- [R package](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/feature-importances-calculation#r-package)\n- [Command-line version](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/feature-importances-calculation#command-line-version)\n- [Related information](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/feature-importances-calculation#related-information)\n\n1. Key Features\n2. Feature importances\n\n# Feature importances\n- [Python package](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/feature-importances-calculation#python-package)\n- [R package](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/feature-importances-calculation#r-package)\n- [Command-line version](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/feature-importances-calculation#command-line-version)\n- [Related information](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/feature-importances-calculation#related-information)\n\nCatBoost provides different types of feature importance calculation:\n\n| Feature importance calculation type | Implementations |\n|---|---|\n| The most important features in the formula | \\- [PredictionValuesChange](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/fstr#regular-feature-importance)   - [LossFunctionChange](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/fstr#regular-feature-importances__lossfunctionchange)   - [InternalFeatureImportance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/fstr#internal-feature-importance) |\n| The contribution of each feature to the formula | [ShapValues](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/shap-values) |\n| The features that work well together | \\- [Interaction](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/feature-interaction#feature-interaction-strength) \\- [InternalInteraction](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/feature-interaction#internal-feature-interaction-strength) |\n\nChoose the implementation for more details.\n\n## Python package\nUse one of the following methods:\n\n- Use the feature\\_importances\\_ attribute.\n- Use one of the following methods to calculate the feature importances after model training:\n\n| Class | Description |\n|---|---|\n| [CatBoost](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/python-reference_catboost) | [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/python-reference_catboost_get_feature_importance) |\n| [CatBoostClassifier](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/python-reference_catboostclassifier) | [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/python-reference_catboostclassifier_get_feature_importance) |\n| [CatBoostRegressor](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/python-reference_catboostregressor) | [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/python-reference_catboostregressor_get_feature_importance) |\n\nThese methods calculate and return the [feature importances](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/fstr).\n\n## R package\nUse one of the following methods:\n\n- Use the `feature_importances`[attribute](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/r-reference#attributes) to get the feature importances.\n- Use one of the following methods to calculate the feature importances after model training:\n\n| Method | Description |\n|---|---|\n| [catboost.get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/r-reference_catboost-get_feature_importance) | Calculate the [feature importances](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/fstr) ([Feature importance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/output-data_feature-analysis_feature-importance) and [Feature interaction strength](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/output-data_feature-analysis_feature-interaction-strength)). |\n## Command-line version\nUse the following command to calculate the feature importances during model training:\n\n| Command | Command keys | Key description |\n|---|---|---|\n| [catboost fit](https:\/\/catboost.ai\/docs\/en\/features\/en\/references\/training-parameters\/) | `--fstr-file` | The name of the resulting file that contains [regular feature importance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/output-data_feature-analysis_feature-importance#per-feature-importance) data (see [Feature importance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/fstr)).   Set the required file name for further feature importance analysis. |\n|   | `--fstr-internal-file` | The name of the resulting file that contains [internal feature importance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/output-data_feature-analysis_feature-importance#internal-feature-importance) data (see [Feature importance](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/fstr)).   Set the required file name for further internal feature importance analysis. |\n\nUse the following command to calculate the feature importances after model training:\n\n| Command | Purpose |\n|---|---|\n| [catboost fstr](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/cli-reference_fstr-calc) | Calculate feature importances. |\n## Related information\n[Model analysis](https:\/\/catboost.ai\/docs\/en\/features\/en\/concepts\/model-analysis)\n\n### Was the article helpful?\nYes\n\nNo\n\nPrevious\n\n[Cross-validation](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/cross-validation)\n\nNext\n\n[User-defined metrics](https:\/\/catboost.ai\/docs\/en\/features\/en\/features\/custom-loss-functions)\n\n![](https:\/\/mc.yandex.ru\/watch\/60763294)","attrs_readable_markdown":"CatBoost provides different types of feature importance calculation:\n\n| Feature importance calculation type | Implementations |\n|---|---|\n| The most important features in the formula | \\- [PredictionValuesChange](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr#regular-feature-importance)   - [LossFunctionChange](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr#regular-feature-importances__lossfunctionchange)   - [InternalFeatureImportance](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr#internal-feature-importance) |\n| The contribution of each feature to the formula | [ShapValues](https:\/\/catboost.ai\/docs\/en\/concepts\/shap-values) |\n| The features that work well together | \\- [Interaction](https:\/\/catboost.ai\/docs\/en\/concepts\/feature-interaction#feature-interaction-strength) \\- [InternalInteraction](https:\/\/catboost.ai\/docs\/en\/concepts\/feature-interaction#internal-feature-interaction-strength) |\n\nChoose the implementation for more details.\n\n## Python package\nUse one of the following methods:\n\n- Use the feature\\_importances\\_ attribute.\n- Use one of the following methods to calculate the feature importances after model training:\n\n| Class | Description |\n|---|---|\n| [CatBoost](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboost) | [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboost_get_feature_importance) |\n| [CatBoostClassifier](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier) | [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostclassifier_get_feature_importance) |\n| [CatBoostRegressor](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostregressor) | [get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/python-reference_catboostregressor_get_feature_importance) |\n\nThese methods calculate and return the [feature importances](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr).\n\n## R package\nUse one of the following methods:\n\n- Use the `feature_importances`[attribute](https:\/\/catboost.ai\/docs\/en\/concepts\/r-reference#attributes) to get the feature importances.\n- Use one of the following methods to calculate the feature importances after model training:\n\n| Method | Description |\n|---|---|\n| [catboost.get\\_feature\\_importance](https:\/\/catboost.ai\/docs\/en\/concepts\/r-reference_catboost-get_feature_importance) | Calculate the [feature importances](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr) ([Feature importance](https:\/\/catboost.ai\/docs\/en\/concepts\/output-data_feature-analysis_feature-importance) and [Feature interaction strength](https:\/\/catboost.ai\/docs\/en\/concepts\/output-data_feature-analysis_feature-interaction-strength)). |\n## Command-line version\nUse the following command to calculate the feature importances during model training:\n\n| Command | Command keys | Key description |\n|---|---|---|\n| [catboost fit](https:\/\/catboost.ai\/docs\/en\/references\/training-parameters\/) | `--fstr-file` | The name of the resulting file that contains [regular feature importance](https:\/\/catboost.ai\/docs\/en\/concepts\/output-data_feature-analysis_feature-importance#per-feature-importance) data (see [Feature importance](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr)).   Set the required file name for further feature importance analysis. |\n|   | `--fstr-internal-file` | The name of the resulting file that contains [internal feature importance](https:\/\/catboost.ai\/docs\/en\/concepts\/output-data_feature-analysis_feature-importance#internal-feature-importance) data (see [Feature importance](https:\/\/catboost.ai\/docs\/en\/concepts\/fstr)).   Set the required file name for further internal feature importance analysis. |\n\nUse the following command to calculate the feature importances after model training:\n\n| Command | Purpose |\n|---|---|\n| [catboost fstr](https:\/\/catboost.ai\/docs\/en\/concepts\/cli-reference_fstr-calc) | Calculate feature importances. |\n\n[Model analysis](https:\/\/catboost.ai\/docs\/en\/concepts\/model-analysis)","meta_canonical":null}

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Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
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Property	Value
URL	https://catboost.ai/docs/en/features/feature-importances-calculation
Last Crawled	2026-04-16 10:05:26 (17 hours ago)
First Indexed	2024-11-14 16:20:31 (1 year ago)
HTTP Status Code	200
Meta Title	Feature importances \| CatBoost
Meta Description	CatBoost provides different types of feature importance calculation: Feature importance calculation type Implementations.
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Boilerpipe Text	CatBoost provides different types of feature importance calculation: Feature importance calculation type Implementations The most important features in the formula - PredictionValuesChange - LossFunctionChange - InternalFeatureImportance The contribution of each feature to the formula ShapValues The features that work well together - Interaction - InternalInteraction Choose the implementation for more details. Python package Use one of the following methods: Use the feature_importances_ attribute. Use one of the following methods to calculate the feature importances after model training: Class Description CatBoost get_feature_importance CatBoostClassifier get_feature_importance CatBoostRegressor get_feature_importance These methods calculate and return the feature importances . R package Use one of the following methods: Use the feature_importances attribute to get the feature importances. Use one of the following methods to calculate the feature importances after model training: Method Description catboost.get_feature_importance Calculate the feature importances ( Feature importance and Feature interaction strength ). Command-line version Use the following command to calculate the feature importances during model training: Command Command keys Key description catboost fit --fstr-file The name of the resulting file that contains regular feature importance data (see Feature importance ). Set the required file name for further feature importance analysis. --fstr-internal-file The name of the resulting file that contains internal feature importance data (see Feature importance ). Set the required file name for further internal feature importance analysis. Use the following command to calculate the feature importances after model training: Command Purpose catboost fstr Calculate feature importances. Model analysis
Markdown	[![Logo icon](https://yastatic.net/s3/locdoc/daas-static/catboost/71b237a322eec6f2889af0dae2a9c549.svg)](https://catboost.ai/ "CatBoost") - Installation - [Overview](https://catboost.ai/docs/en/features/en/concepts/installation) - Python package installation - CatBoost for Apache Spark installation - R package installation - Command-line version binary - Build from source - Key Features - [Training](https://catboost.ai/docs/en/features/en/features/training) - [Training on GPU](https://catboost.ai/docs/en/features/en/features/training-on-gpu) - [Regular prediction](https://catboost.ai/docs/en/features/en/features/prediction) - [Staged prediction](https://catboost.ai/docs/en/features/en/features/staged-prediction) - [Cross-validation](https://catboost.ai/docs/en/features/en/features/cross-validation) - [Feature importances](https://catboost.ai/docs/en/features/en/features/feature-importances-calculation) - [User-defined metrics](https://catboost.ai/docs/en/features/en/features/custom-loss-functions) - [Using the overfitting detector](https://catboost.ai/docs/en/features/en/features/overfitting-detector-desc) - [Export a model to CoreML](https://catboost.ai/docs/en/features/en/features/export-model-to-core-ml) - [Pre-trained data](https://catboost.ai/docs/en/features/en/features/proceed-training) - [Calculate metrics](https://catboost.ai/docs/en/features/en/features/eval-metrics) - [Categorical features](https://catboost.ai/docs/en/features/en/features/categorical-features) - [Text features](https://catboost.ai/docs/en/features/en/features/text-features) - [Embeddings features](https://catboost.ai/docs/en/features/en/features/embeddings-features) - [Aggregated graph features](https://catboost.ai/docs/en/features/en/features/graph-aggregated-features) - [Implemented metrics](https://catboost.ai/docs/en/features/en/features/loss-functions-desc) - [Export a model to Python or C++](https://catboost.ai/docs/en/features/en/features/export-model-to-python) - [Export a model to JSON](https://catboost.ai/docs/en/features/en/features/export-model-to-json) - [Object importances](https://catboost.ai/docs/en/features/en/features/object-importances-calcution) - Training parameters - Python package - 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/features/en/concepts/parameter-tuning) - [Speeding up the training](https://catboost.ai/docs/en/features/en/concepts/speed-up-training) - Data visualization - Algorithm details - [FAQ](https://catboost.ai/docs/en/features/en/concepts/faq) - Educational materials - [Development and contributions](https://catboost.ai/docs/en/features/en/concepts/development-and-contributions) - [Contacts](https://catboost.ai/docs/en/features/en/concepts/contacts) Feature importances ## In this article: - [Python package](https://catboost.ai/docs/en/features/en/features/feature-importances-calculation#python-package) - [R package](https://catboost.ai/docs/en/features/en/features/feature-importances-calculation#r-package) - [Command-line version](https://catboost.ai/docs/en/features/en/features/feature-importances-calculation#command-line-version) - [Related information](https://catboost.ai/docs/en/features/en/features/feature-importances-calculation#related-information) 1. Key Features 2. Feature importances # Feature importances - [Python package](https://catboost.ai/docs/en/features/en/features/feature-importances-calculation#python-package) - [R package](https://catboost.ai/docs/en/features/en/features/feature-importances-calculation#r-package) - [Command-line version](https://catboost.ai/docs/en/features/en/features/feature-importances-calculation#command-line-version) - [Related information](https://catboost.ai/docs/en/features/en/features/feature-importances-calculation#related-information) CatBoost provides different types of feature importance calculation: \| Feature importance calculation type \| Implementations \| \|---\|---\| \| The most important features in the formula \| \- [PredictionValuesChange](https://catboost.ai/docs/en/features/en/concepts/fstr#regular-feature-importance) - [LossFunctionChange](https://catboost.ai/docs/en/features/en/concepts/fstr#regular-feature-importances__lossfunctionchange) - [InternalFeatureImportance](https://catboost.ai/docs/en/features/en/concepts/fstr#internal-feature-importance) \| \| The contribution of each feature to the formula \| [ShapValues](https://catboost.ai/docs/en/features/en/concepts/shap-values) \| \| The features that work well together \| \- [Interaction](https://catboost.ai/docs/en/features/en/concepts/feature-interaction#feature-interaction-strength) \- [InternalInteraction](https://catboost.ai/docs/en/features/en/concepts/feature-interaction#internal-feature-interaction-strength) \| Choose the implementation for more details. ## Python package Use one of the following methods: - Use the feature\_importances\_ attribute. - Use one of the following methods to calculate the feature importances after model training: \| Class \| Description \| \|---\|---\| \| [CatBoost](https://catboost.ai/docs/en/features/en/concepts/python-reference_catboost) \| [get\_feature\_importance](https://catboost.ai/docs/en/features/en/concepts/python-reference_catboost_get_feature_importance) \| \| [CatBoostClassifier](https://catboost.ai/docs/en/features/en/concepts/python-reference_catboostclassifier) \| [get\_feature\_importance](https://catboost.ai/docs/en/features/en/concepts/python-reference_catboostclassifier_get_feature_importance) \| \| [CatBoostRegressor](https://catboost.ai/docs/en/features/en/concepts/python-reference_catboostregressor) \| [get\_feature\_importance](https://catboost.ai/docs/en/features/en/concepts/python-reference_catboostregressor_get_feature_importance) \| These methods calculate and return the [feature importances](https://catboost.ai/docs/en/features/en/concepts/fstr). ## R package Use one of the following methods: - Use the `feature_importances`[attribute](https://catboost.ai/docs/en/features/en/concepts/r-reference#attributes) to get the feature importances. - Use one of the following methods to calculate the feature importances after model training: \| Method \| Description \| \|---\|---\| \| [catboost.get\_feature\_importance](https://catboost.ai/docs/en/features/en/concepts/r-reference_catboost-get_feature_importance) \| Calculate the [feature importances](https://catboost.ai/docs/en/features/en/concepts/fstr) ([Feature importance](https://catboost.ai/docs/en/features/en/concepts/output-data_feature-analysis_feature-importance) and [Feature interaction strength](https://catboost.ai/docs/en/features/en/concepts/output-data_feature-analysis_feature-interaction-strength)). \| ## Command-line version Use the following command to calculate the feature importances during model training: \| Command \| Command keys \| Key description \| \|---\|---\|---\| \| [catboost fit](https://catboost.ai/docs/en/features/en/references/training-parameters/) \| `--fstr-file` \| The name of the resulting file that contains [regular feature importance](https://catboost.ai/docs/en/features/en/concepts/output-data_feature-analysis_feature-importance#per-feature-importance) data (see [Feature importance](https://catboost.ai/docs/en/features/en/concepts/fstr)). Set the required file name for further feature importance analysis. \| \| \| `--fstr-internal-file` \| The name of the resulting file that contains [internal feature importance](https://catboost.ai/docs/en/features/en/concepts/output-data_feature-analysis_feature-importance#internal-feature-importance) data (see [Feature importance](https://catboost.ai/docs/en/features/en/concepts/fstr)). Set the required file name for further internal feature importance analysis. \| Use the following command to calculate the feature importances after model training: \| Command \| Purpose \| \|---\|---\| \| [catboost fstr](https://catboost.ai/docs/en/features/en/concepts/cli-reference_fstr-calc) \| Calculate feature importances. \| ## Related information [Model analysis](https://catboost.ai/docs/en/features/en/concepts/model-analysis) ### Was the article helpful? 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Readable Markdown	CatBoost provides different types of feature importance calculation: \| Feature importance calculation type \| Implementations \| \|---\|---\| \| The most important features in the formula \| \- [PredictionValuesChange](https://catboost.ai/docs/en/concepts/fstr#regular-feature-importance) - [LossFunctionChange](https://catboost.ai/docs/en/concepts/fstr#regular-feature-importances__lossfunctionchange) - [InternalFeatureImportance](https://catboost.ai/docs/en/concepts/fstr#internal-feature-importance) \| \| The contribution of each feature to the formula \| [ShapValues](https://catboost.ai/docs/en/concepts/shap-values) \| \| The features that work well together \| \- [Interaction](https://catboost.ai/docs/en/concepts/feature-interaction#feature-interaction-strength) \- [InternalInteraction](https://catboost.ai/docs/en/concepts/feature-interaction#internal-feature-interaction-strength) \| Choose the implementation for more details. ## Python package Use one of the following methods: - Use the feature\_importances\_ attribute. - Use one of the following methods to calculate the feature importances after model training: \| Class \| Description \| \|---\|---\| \| [CatBoost](https://catboost.ai/docs/en/concepts/python-reference_catboost) \| [get\_feature\_importance](https://catboost.ai/docs/en/concepts/python-reference_catboost_get_feature_importance) \| \| [CatBoostClassifier](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier) \| [get\_feature\_importance](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_get_feature_importance) \| \| [CatBoostRegressor](https://catboost.ai/docs/en/concepts/python-reference_catboostregressor) \| [get\_feature\_importance](https://catboost.ai/docs/en/concepts/python-reference_catboostregressor_get_feature_importance) \| These methods calculate and return the [feature importances](https://catboost.ai/docs/en/concepts/fstr). ## R package Use one of the following methods: - Use the `feature_importances`[attribute](https://catboost.ai/docs/en/concepts/r-reference#attributes) to get the feature importances. - Use one of the following methods to calculate the feature importances after model training: \| Method \| Description \| \|---\|---\| \| [catboost.get\_feature\_importance](https://catboost.ai/docs/en/concepts/r-reference_catboost-get_feature_importance) \| Calculate the [feature importances](https://catboost.ai/docs/en/concepts/fstr) ([Feature importance](https://catboost.ai/docs/en/concepts/output-data_feature-analysis_feature-importance) and [Feature interaction strength](https://catboost.ai/docs/en/concepts/output-data_feature-analysis_feature-interaction-strength)). \| ## Command-line version Use the following command to calculate the feature importances during model training: \| Command \| Command keys \| Key description \| \|---\|---\|---\| \| [catboost fit](https://catboost.ai/docs/en/references/training-parameters/) \| `--fstr-file` \| The name of the resulting file that contains [regular feature importance](https://catboost.ai/docs/en/concepts/output-data_feature-analysis_feature-importance#per-feature-importance) data (see [Feature importance](https://catboost.ai/docs/en/concepts/fstr)). Set the required file name for further feature importance analysis. \| \| \| `--fstr-internal-file` \| The name of the resulting file that contains [internal feature importance](https://catboost.ai/docs/en/concepts/output-data_feature-analysis_feature-importance#internal-feature-importance) data (see [Feature importance](https://catboost.ai/docs/en/concepts/fstr)). Set the required file name for further internal feature importance analysis. \| Use the following command to calculate the feature importances after model training: \| Command \| Purpose \| \|---\|---\| \| [catboost fstr](https://catboost.ai/docs/en/concepts/cli-reference_fstr-calc) \| Calculate feature importances. \| [Model analysis](https://catboost.ai/docs/en/concepts/model-analysis)
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