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.

The higher the deviation from 0, the bigger the impact that an object has on the optimized metric.

The method is an implementation of the approach described in the Finding Influential Training Samples for Gradient Boosted Decision Trees paper .

Method call format



Parameter Possible types Description Default value
pool catboost.Pool The data for calculating object importances. Required parameter
train_pool catboost.Pool

The dataset used for training.

Required parameter
top_size int

Defines the number of most important objects from the training dataset. The number of returned objects is limited to this number.

-1 (top size is not limited)
type int

The method for calculating the object importances.

Possible values:
  • Average — The average of scores of objects from the training dataset for every object from the input dataset.
  • PerObject — The scores of each object from the training dataset for each object from the input dataset.
update_method string

The algorithm accuracy method.

Possible values:
  • SinglePoint — The fastest and least accurate method.
  • TopKLeaves — Specify the number of leaves. The higher the value, the more accurate and the slower the calculation.
  • AllPoints — The slowest and most accurate method.
Supported parameters:
For example, the following value sets the method to TopKLeaves and limits the number of leaves to 3: