# KernelScoringExplainer class

## Definition

Defines a scoring model based on KernelExplainer.

`KernelScoringExplainer(original_explainer, initialization_examples=None, **kwargs)`

- Inheritance

## 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.

#### 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

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