KernelScoringExplainer class

Definition

Defines a scoring model based on KernelExplainer.

KernelScoringExplainer(original_explainer, initialization_examples=None, **kwargs)
Inheritance
azureml._logging.chained_identity.ChainedIdentity
KernelScoringExplainer

Methods

explain(evaluation_examples, get_raw=None)

Use the TreeExplainer and tree model for scoring to get the feature importance values of data.

explain(evaluation_examples, get_raw=None)

Use the TreeExplainer and tree model for scoring to get the feature importance values of data.

explain(evaluation_examples, get_raw=None)

Parameters

evaluation_examples
numpy.array or DataFrame or scipy.sparse.csr_matrix

A matrix of feature vector examples (# examples x # features) on which to explain the model's output.

get_raw
bool

If True, importances for raw features will be returned. If False, importances for engineered features will be returned. If unspecified and transformations was passed into the original explainer, raw importances will be returned. If unspecified and feature_maps was passed into the scoring explainer, engineered importances will be returned.

default value: None

Returns

For a model with a single output such as regression, this returns a matrix of feature importance values. For models with vector outputs this function returns a list of such matrices, one for each output. The dimension of this matrix is (# examples x # features).

Return type