Feature importances
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_importancesattribute 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. |