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Gini Index

skfeature.function.statistical_based.gini_index

Description

Gini Index evaluates features using the Gini impurity measure. Lower Gini values indicate features that separate the classes more cleanly, so features are ranked by impurity.

Usage

import numpy as np
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectKBest

from skfeature.function.statistical_based import gini_index

X, y = load_iris(return_X_y=True)

# rank features and select the top k
selector = SelectKBest(score_func=gini_index.gini_index, k=2)
X_selected = selector.fit_transform(X, y)

Parameters

  • X: numpy array, shape (n_samples, n_features) — input data
  • y: numpy array, shape (n_samples,) — class labels
  • mode: {{"rank", "index"}}, default "rank" — "rank" returns an array of feature indices ordered by importance and aligned with sklearn.feature_selection.SelectKBest; "index" returns the indices of the selected features with the most important one first

Returns

  • score: numpy array, shape (n_features,) — ranking score of every feature, aligned with sklearn.feature_selection.SelectKBest

References

  • Original implementation from the DMML Lab@ASU Feature Selection Repository.