# eli5.permutation_importance¶

A module for computing feature importances by measuring how score decreases when a feature is not available. It contains basic building blocks; there is a full-featured sklearn-compatible implementation in PermutationImportance.

A similar method is described in Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001 (available online at https://www.stat.berkeley.edu/%7Ebreiman/randomforest2001.pdf), with an application to random forests. It is known in literature as “Mean Decrease Accuracy (MDA)” or “permutation importance”.

get_score_importances(score_func, X, y, n_iter=5, columns_to_shuffle=None, random_state=None)[source]

Return (base_score, score_decreases) tuple with the base score and score decreases when a feature is not available.

base_score is score_func(X, y); score_decreases is a list of length n_iter with feature importance arrays (each array is of shape n_features); feature importances are computed as score decrease when a feature is not available.

n_iter iterations of the basic algorithm is done, each iteration starting from a different random seed.

If you just want feature importances, you can take a mean of the result:

import numpy as np
from eli5.permutation_importance import get_scores_importances

base_score, score_decreases = get_score_importances(score_func, X, y)
feature_importances = np.mean(score_decreases, axis=0)

iter_shuffled(X, columns_to_shuffle=None, pre_shuffle=False, random_state=None)[source]

Return an iterator of X matrices which have one or more columns shuffled. After each iteration yielded matrix is mutated inplace, so if you want to use multiple of them at the same time, make copies.

columns_to_shuffle is a sequence of column numbers to shuffle. By default, all columns are shuffled once, i.e. columns_to_shuffle is range(X.shape[1]).

If pre_shuffle is True, a copy of X is shuffled once, and then result takes shuffled columns from this copy. If it is False, columns are shuffled on fly. pre_shuffle = True can be faster if there is a lot of columns, or if columns are used multiple times.