Fisher Score¶

skfeature.function.similarity_based.fisher_score

Description¶

Fisher Score is a supervised ranking method. For each feature it measures the ratio of between-class variance to within-class variance; features with high scores are discriminative and are ranked first.

Usage¶

import numpy as np
from sklearn.datasets import load_iris

from skfeature.function.similarity_based import fisher_score

X, y = load_iris(return_X_y=True)

# get a score for every feature (aligned with SelectKBest)
score = fisher_score.fisher_score(X, y)

# or get the indices of the selected features
selected = fisher_score.fisher_score(X, y, mode="index")

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.