- The `mlxtend.preprocessing.standardize` function now optionally returns the parameters, which are estimated from the array, for re-use. A further improvement makes the `standardize` function smarter in order to avoid zero-division errors
- Added a progress bar tracker to `classifier.NeuralNetMLP`
- Added a function to score predicted vs. target class labels `evaluate.scoring`
- Added confusion matrix functions to create (`evaluate.confusion_matrix`) and plot (`evaluate.plot_confusion_matrix`) confusion matrices
- Cosmetic improvements to the `evaluate.plot_decision_regions` function such as hiding plot axes
- Renaming of `classifier.EnsembleClassfier` to `classifier.EnsembleVoteClassifier`
- Improved random weight initialization in `Perceptron`, `Adaline`, `LinearRegression`, and `LogisticRegression`
- Changed `learning` parameter of `mlxtend.classifier.Adaline` to solver and added "normal equation" as closed-form solution solver
- New style parameter and improved axis scaling in `mlxtend.evaluate.plot_learning_curves`
- Hide y-axis labels in `mlxtend.evaluate.plot_decision_regions` in 1 dimensional evaluations
- Added `loadlocal_mnist` to `mlxtend.data` for streaming MNIST from a local byte files into numpy arrays
- New `NeuralNetMLP` parameters: `random_weights`, `shuffle_init`, `shuffle_epoch`
- Sequential Feature Selection algorithms were unified into a single `SequentialFeatureSelector` class with parameters to enable floating selection and toggle between forward and backward selection.
- New `SFS` features such as the generation of pandas `DataFrame` results tables and plotting functions (with confidence intervals, standard deviation, and standard error bars)
- Added support for regression estimators in `SFS`
- Stratified sampling of MNIST (now 500x random samples from each of the 10 digit categories)
- Added Boston `housing dataset`
- Renaming `mlxtend.plotting` to `mlxtend.general_plotting` in order to distinguish general plotting function from specialized utility function such as `evaluate.plot_decision_regions`
- Shuffle fix and new shuffle parameter for classifier.NeuralNetMLP