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t-Score

skfeature.function.statistical_based.t_score

Description

t-Score ranks features using a two-sample t-test style statistic for each feature across the class labels, favoring features whose class means are well separated relative to the within-class spread.

Usage

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

from skfeature.function.statistical_based import t_score

X, y = load_iris(return_X_y=True)

# rank features and select the top k
selector = SelectKBest(score_func=t_score.t_score, 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.