CatBoost is a state-of-the-art open-source gradient boosting on decision trees library. eli5 supports eli5.explain_weights() for catboost.CatBoost, catboost.CatBoostClassifier and catboost.CatBoostRegressor.

eli5.explain_weights() uses feature importances. Additional arguments for CatBoostClassifier and CatBoostRegressor:

  • importance_type is a way to get feature importance. Possible values are:
    • ‘PredictionValuesChange’ - The individual importance values for each of the input features.(default)
    • ‘LossFunctionChange’ - The individual importance values for each of the input features for ranking metrics (requires training data to be passed or a similar dataset with Pool)
  • pool the catboost.Pool datatype . To be passed if explain_weights_catboost has importance_type set to ‘LossFunctionChange’. The catboost feature_importances uses the Pool datatype to calculate the parameter for the specific importance_type.


Top-level eli5.explain_weights() calls are dispatched to eli5.catboost.explain_weights_catboost() for catboost.CatBoost, catboost.CatBoostClassifer and catboost.CatBoostRegressor.