Version 0.19.0

Version 0.19.0 (09/02/2021)

New Features

• Adds a second "balanced accuracy" interpretation ("balanced") to evaluate.accuracy_score in addition to the existing "average" option to compute the scikit-learn-style balanced accuracy. (#764)
• Adds new scatter_hist function to mlxtend.plotting for generating a scattered histogram. (#757 via Maitreyee Mhasaka)
• The evaluate.permutation_test function now accepts a paired argument to specify to support paired permutation/randomization tests. (#768)
• The StackingCVRegressor now also supports multi-dimensional targets similar to StackingRegressor via StackingCVRegressor(..., multi_output=True). (#802 via Marco Tiraboschi)

Changes

• Updates unit tests for scikit-learn 0.24.1 compatibility. (#774)
• StackingRegressor now requires setting StackingRegressor(..., multi_output=True) if the target is multi-dimensional; this allows for better input validation. (#802)
• Removes deprecated res argument from plot_decision_regions. (#803)
• Adds a title_fontsize parameter to plot_learning_curves for controlling the title font size; also the plot style is now the matplotlib default. (#818)
• Internal change using 'c': 'none' instead of 'c': '' in mlxtend.plotting.plot_decision_regions's scatterplot highlights to stay compatible with Matplotlib 3.4 and newer. (#822)
• Adds a fontcolor_threshold parameter to the mlxtend.plotting.plot_confusion_matrix function as an additional option for determining the font color cut-off manually. (#827)
• The frequent_patterns.association_rules now raises a ValueError if an empty frequent itemset DataFrame is passed. (#843)
• The .632 and .632+ bootstrap method implemented in the mlxtend.evaluate.bootstrap_point632_score function now use the whole training set for the resubstitution weighting term instead of the internal training set that is a new bootstrap sample in each round. (#844)

Bug Fixes

• Fixes a typo in the SequentialFeatureSelector documentation (#835 via João Pedro Zanlorensi Cardoso)