Version 0.18.0

New Features

• The bias_variance_decomp function now supports optional fit_params for the estimators that are fit on bootstrap samples. (#748)
• The bias_variance_decomp function now supports Keras estimators. (#725 via @hanzigs)
• Adds new mlxtend.classifier.OneRClassifier (One Rule Classfier) class, a simple rule-based classifier that is often used as a performance baseline or simple interpretable model. (#726
• Adds new create_counterfactual method for creating counterfactuals to explain model predictions. (#740)

Changes

• permutation_test (mlxtend.evaluate.permutation) ìs corrected to give the proportion of permutations whose statistic is at least as extreme as the one observed. (#721 via Florian Charlier)
• Fixes the McNemar confusion matrix layout to match the convention (and documentation), swapping the upper left and lower right cells. (#744 via mmarius)

Bug Fixes

• The loss in LogisticRegression for logging purposes didn't include the L2 penalty for the first weight in the weight vector (this is not the bias unit). However, since this loss function was only used for logging purposes, and the gradient remains correct, this does not have an effect on the main code. (#741)
• Fixes a bug in bias_variance_decomp where when the mse loss was used, downcasting to integers caused imprecise results for small numbers. (#749)