get_feature_importance

Calculate and return the feature importances.

Method call formatMethod call format

get_feature_importance(data=None,
                       reference_data=None,
                       type=EFstrType.FeatureImportance,
                       prettified=False,
                       thread_count=-1,
                       verbose=False,
                       log_cout=sys.stdout,
                       log_cerr=sys.stderr)

ParametersParameters

datadata

DescriptionDescription

The dataset for feature importance calculation.

The required dataset depends on the selected feature importance calculation type (specified in the type parameter):

• PredictionValuesChange — Either None or the same dataset that was used for training if the model does not contain information regarding the weight of leaves. All models trained with CatBoost version 0.9 or higher contain leaf weight information by default.
• LossFunctionChange  — Any dataset. Feature importances are calculated on a subset for large datasets.
• PredictionDiff — A list of object pairs.

Possible types

catboost.Pool

Default value

Required parameter for the LossFunctionChange and ShapValues type of feature importances and in case the model does not contain information regarding the weight of leaves.

reference_datareference_data

DescriptionDescription

Reference data for Independent Tree SHAP values from Explainable AI for Trees: From Local Explanations to Global Understanding. If type is ShapValues and reference_data is not None, then Independent Tree SHAP values are calculated.

Possible types

catboost.Pool

Default value

None

typetype

Alias: fstr_type (deprecated, use type instead)

DescriptionDescription

The type of feature importance to calculate.

Possible values:

• FeatureImportance: Equal to PredictionValuesChange for non-ranking metrics and LossFunctionChange for ranking metrics (the value is determined automatically).
• ShapValues: A vector with contributions of each feature to the prediction for every input object and the expected value of the model prediction for the object (average prediction given no knowledge about the object).
• Interaction: The value of the feature interaction strength for each pair of features.
• PredictionDiff: A vector with contributions of each feature to the RawFormulaVal difference for each pair of objects.

Possible types

catboost.Pool

Default value

FeatureImportance

prettifiedprettified

DescriptionDescription

Return the feature importances as a list of the following pairs sorted by feature importance:

(feature_id, feature importance)

Should be used if one of the following values of the typeparameter is selected:

• PredictionValuesChange
• PredictionValuesChange

Possible types

bool

Default value
False

thread_countthread_count

DescriptionDescription

The number of threads to calculate feature importance.

Optimizes the speed of execution. This parameter doesn't affect results.

Possible types int

Default value

-1 (the number of threads is equal to the number of processor cores)

verboseverbose

DescriptionDescription

The purpose of this parameter depends on the type of the given value:

• bool — Output progress to stdout.
  Works with the ShapValues type of feature importance calculation.
• int — The logging period.

Possible types

• bool
• int

Default value

False

log_coutlog_cout

Output stream or callback for logging.

Possible types

• callable Python object
• python object providing the write() method

Default value

sys.stdout

log_cerrlog_cerr

Error stream or callback for logging.

Possible types

• callable Python object
• python object providing the write() method

Default value

sys.stderr

Type of return valueType of return value

Depends on the selected feature strength calculation method:

• PredictionValuesChange, LossFunctionChange or PredictionValuesChange with the prettified parameter set to False: a list of length [n_features] with float feature importances values for each feature
• PredictionValuesChange or LossFunctionChange with the prettified parameter set to True: a list of length [n_features] with (feature_id (string), feature_importance (float)) pairs, sorted by feature importance values in descending order
• ShapValues: np.array of shape (n_objects, n_features + 1) with float ShapValues for each (object, feature)
• Interaction: list of length [ n_features] of three element lists of (first_feature_index, second_feature_index, interaction_score (float))