v0.5.0

Version 0.5.0

Downloads

• Source code (zip)
• Source code (tar.gz)

New Features

• New ExhaustiveFeatureSelector estimator in mlxtend.feature_selection for evaluating all feature combinations in a specified range
• The StackingClassifier has a new parameter average_probas that is set to True by default to maintain the current behavior. A deprecation warning was added though, and it will default to False in future releases (0.6.0); average_probas=False will result in stacking of the level-1 predicted probabilities rather than averaging these.
• New StackingCVClassifier estimator in 'mlxtend.classifier' for implementing a stacking ensemble that uses cross-validation techniques for training the meta-estimator to avoid overfitting (Reiichiro Nakano)
• New OnehotTransactions encoder class added to the preprocessing submodule for transforming transaction data into a one-hot encoded array
• The SequentialFeatureSelector estimator in mlxtend.feature_selection now is safely stoppable mid-process by control+c, and deprecated print_progress in favor of a more tunable verbose parameter (Will McGinnis)
• New apriori function in association to extract frequent itemsets from transaction data for association rule mining
• New checkerboard_plot function in plotting to plot checkerboard tables / heat maps
• New mcnemar_table and mcnemar functions in evaluate to compute 2x2 contingency tables and McNemar's test

Changes

• All plotting functions have been moved to mlxtend.plotting for compatibility reasons with continuous integration services and to make the installation of matplotlib optional for users of mlxtend's core functionality
• Added a compatibility layer for scikit-learn 0.18 using the new model_selection module while maintaining backwards compatibility to scikit-learn 0.17.

Bug Fixes

• mlxtend.plotting.plot_decision_regions now draws decision regions correctly if more than 4 class labels are present
• Raise AttributeError in plot_decision_regions when the X_higlight argument is a 1D array (chkoar)