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Decision Tree Backward

skfeature.function.wrapper.decision_tree_backward

Description

Decision Tree Backward is a wrapper method that starts from the full feature set and greedily removes the least useful feature at each step, based on the cross-validated accuracy of a Decision Tree classifier.

Usage

import numpy as np
from sklearn.datasets import load_iris

from skfeature.function.wrapper import decision_tree_backward

X, y = load_iris(return_X_y=True)

# rank features by cross-validated classifier performance
score = decision_tree_backward.decision_tree_backward(X, y)

Parameters

  • X: numpy array, shape (n_samples, n_features) — input data
  • y: numpy array, shape (n_samples,) — class labels
  • n_selected_features: int — number of features to select
  • mode: {"rank", "index"}, default "rank"

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.