Version 0.8.0

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New Features

• Added a mlxtend.evaluate.bootstrap that implements the ordinary nonparametric bootstrap to bootstrap a single statistic (for example, the mean. median, R^2 of a regression fit, and so forth) #232
• SequentialFeatureSelecor's k_features now accepts a string argument "best" or "parsimonious" for more "automated" feature selection. For instance, if "best" is provided, the feature selector will return the feature subset with the best cross-validation performance. If "parsimonious" is provided as an argument, the smallest feature subset that is within one standard error of the cross-validation performance will be selected. #238

Changes

• SequentialFeatureSelector now uses np.nanmean over normal mean to support scorers that may return np.nan #211 (via mrkaiser)
• The skip_if_stuck parameter was removed from SequentialFeatureSelector in favor of a more efficient implementation comparing the conditional inclusion/exclusion results (in the floating versions) to the performances of previously sampled feature sets that were cached #237
• ExhaustiveFeatureSelector was modified to consume substantially less memory #195 (via Adam Erickson)

Bug Fixes

• Fixed a bug where the SequentialFeatureSelector selected a feature subset larger than then specified via the k_features tuple max-value #213