Object importances

CatBoost provides the following types of object importances calculation:

• Average
• PerObject

Choose the implementation for more details.

Python package

Classes:

CatBoost

Class purpose

Training and applying models.

Method

get_object_importance

CatBoostClassifier

Class purpose

Training and applying models.

Method

get_object_importance

CatBoostRegressor

Class purpose

Training and applying models.

Method

get_object_importance

R package

Method

catboost.get_object_importance

Purpose

Calculate the effect of objects from the train dataset on the optimized metric values for the objects from the input dataset:

• Positive values reflect that the optimized metric increases.
• Negative values reflect that the optimized metric decreases.

Command-line version

Command

catboost ostr

Purpose

Calculate the effect of objects from the training dataset on the optimized metric values for the objects from the validation dataset:

• Positive values reflect that the optimized metric increases.
• Negative values reflect that the optimized metric decreases.
  The higher the deviation from 0, the bigger the impact that an object has on the optimized metric.