hidimstat.BaseVariableImportance

class hidimstat.BaseVariableImportance

Bases: BaseEstimator

Base class for variable importance methods.

This class provides a foundation for implementing variable importance methods, including feature selection based on importance scores and p-values.

Attributes:

importances_array-like of shape (n_features,), default=None

The computed importance scores for each feature.

pvalues_array-like of shape (n_features,), default=None

The computed p-values for each feature.

selections_array-like of shape (n_features,), default=None

Binary mask indicating selected features.

Methods

selection(k_best=None, percentile=None, threshold=None, threshold_pvalue=None)	Selects features based on importance scores and/or p-values using various criteria.
_check_importance()	Checks if importance scores and p-values have been computed.

selection(k_best=None, percentile=None, threshold=None, threshold_pvalue=None)

Selects features based on variable importance. In case several arguments are different from None, the returned selection is the conjunction of all of them.

Parameters:

k_bestint, optional, default=None

Selects the top k features based on importance scores.

percentilefloat, optional, default=None

Selects features based on a specified percentile of importance scores.

thresholdfloat, optional, default=None

Selects features with importance scores above the specified threshold.

threshold_pvaluefloat, optional, default=None

Selects features with p-values below the specified threshold.

Returns:

selectionarray-like of shape (n_features,)

Binary array indicating the selected features.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.