The following process describes the estimation of out-of-bag predictor importance values by permutation. Furthermore, the impurity-based feature importance of random forests suffers from being computed on statistics derived from the training dataset: the importances can be high even for features that are not predictive of the target variable, as long as the model has the capacity to use them to overfit. The permutation feature importance measurement was introduced by Breiman (2001) 35 for random forests. X can be the data set used to train the estimator or a hold-out set. sklearn.inspection.permutation_importance¶ sklearn.inspection.permutation_importance (estimator, X, y, *, scoring=None, n_repeats=5, n_jobs=None, random_state=None) [source] ¶ Permutation importance for feature evaluation .. Record a baseline accuracy (classifier) or R 2 score (regressor) by passing a validation set or the out-of-bag (OOB) samples through the random forest. While the permutation importance approach yields results that are generally consistent with the mean impurity decrease feature importance values from a random forest, it's a method that is model-agnostic and can be used with any kind of classifier or regressor. Based on this idea, Fisher, Rudin, and Dominici (2018) 36 proposed a model-agnostic version of the feature importance and called it model reliance. The R Random Forest package implements both the Gini and the Permutation importance. The CRAN implementation of random forests offers both variable importance measures: the Gini importance as well as the widely used permutation importance defined as For classification, it is the increase in percent of times a case is OOB and misclassified when the variable is permuted. In fact, the RF importance technique we'll introduce here (permutation importance) is applicable to any model, though few machine learning practitioners seem to realize this. The estimator is required to be a fitted estimator. Suppose that R is a random forest of T learners and … In the case of classification, the R Random Forest package also shows feature performance for each class. This is useful to detect features that would degrade performace for a specific class while being positive on average.

Permutation importance is a common, reasonably efficient, and very reliable technique. Most random Forest (RF) implementations also provide measures of feature importance. A more reliable method is permutation importance, which measures the importance of a feature as follows.