New Features
- New [mlxtend.plotting.ecdf](http://rasbt.github.io/mlxtend/user_guide/plotting/ecdf/) function for plotting empirical cumulative distribution functions ([#196](https://github.com/rasbt/mlxtend/pull/196)).
- New [`StackingCVRegressor`](http://rasbt.github.io/mlxtend/user_guide/regressor/StackingCVRegressor/) for stacking regressors with out-of-fold predictions to prevent overfitting ([#201](https://github.com/rasbt/mlxtend/pull/201)via [Eike Dehling](https://github.com/EikeDehling)).
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](https://github.com/rasbt/mlxtend/pull/189), via [James Bourbeau](https://github.com/jrbourbeau)).
- 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](https://github.com/rasbt/mlxtend/pull/193), via [whalebot-helmsman](https://github.com/whalebot-helmsman)).
- Raise meaningful error messages if pandas `DataFrame`s or Python lists of lists are fed into the `StackingCVClassifer` as a `fit` arguments ([198](https://github.com/rasbt/mlxtend/pull/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](https://github.com/rasbt/mlxtend/pull/203), via [Konstantinos Paliouras](https://github.com/sque)).
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](https://github.com/rasbt/mlxtend/pull/171)).
- The `plot_decision_regions` function now supports pre-existing axes objects generated via matplotlib's `plt.subplots`. ([184](https://github.com/rasbt/mlxtend/pull/184), [see example](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/#example-6-working-with-existing-axes-objects-using-subplots))
- Made `math.num_combinations` and `math.num_permutations` numerically stable for large numbers of combinations and permutations ([200](https://github.com/rasbt/mlxtend/pull/200)).