Version 0.7.0

Version 0.7.0 (2017-06-22)

New Features

• New mlxtend.plotting.ecdf function for plotting empirical cumulative distribution functions (#196).
• New StackingCVRegressor for stacking regressors with out-of-fold predictions to prevent overfitting (#201via Eike Dehling).

Changes

• The TensorFlow estimator have been removed from mlxtend, since TensorFlow has now very convenient ways to build on estimators, which render those implementations obsolete.
• plot_decision_regions now supports plotting decision regions for more than 2 training features #189, via James Bourbeau).
• Parallel execution in mlxtend.feature_selection.SequentialFeatureSelector and mlxtend.feature_selection.ExhaustiveFeatureSelector is now performed over different feature subsets instead of the different cross-validation folds to better utilize machines with multiple processors if the number of features is large (#193, via @whalebot-helmsman).
• Raise meaningful error messages if pandas DataFrames or Python lists of lists are fed into the StackingCVClassifer as a fit arguments (198).
• The n_folds parameter of the StackingCVClassifier was changed to cv and can now accept any kind of cross validation technique that is available from scikit-learn. For example, StackingCVClassifier(..., cv=StratifiedKFold(n_splits=3)) or StackingCVClassifier(..., cv=GroupKFold(n_splits=3)) (#203, via Konstantinos Paliouras).

Bug Fixes

• SequentialFeatureSelector now correctly accepts a None argument for the scoring parameter to infer the default scoring metric from scikit-learn classifiers and regressors (#171).
• The plot_decision_regions function now supports pre-existing axes objects generated via matplotlib's plt.subplots. (#184, see example)
• Made math.num_combinations and math.num_permutations numerically stable for large numbers of combinations and permutations (#200).