Chi-Square¶

skfeature.function.statistical_based.chi_square

Description¶

Chi-Square measures the dependence between each feature and the class labels using the chi-square statistic. It is a standard filter for classification and is well suited to count-based or non-negative features.

Usage¶

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

from skfeature.function.statistical_based import chi_square

X, y = load_iris(return_X_y=True)

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

Parameters¶

X: numpy array, shape (n_samples, n_features) — input data, must be non-negative
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¶

Based on sklearn.feature_selection.chi2.