Version 0.10.0

ADD #325: Allow to separately optimize metrics for metadata generation.
ADD #946: New dask backend for parallel Auto-sklearn.
BREAKING #947: Drop Python3.5 support.
BREAKING #946: Remove shared model mode for parallel Auto-sklearn.
FIX #351: No longer pass un-picklable logger instances to the target function.
FIX #840: Fixes a bug which prevented computing metadata for regression datasets. Also adds a unit test for regression metadata computation.
FIX #897: Allow custom splitters to be used with multi-ouput regression.
FIX #951: Fixes a lot of bugs in the regression pipeline that caused bad performance for regression datasets.
FIX #953: Re-add liac-arff as a dependency.
FIX #956: Fixes a bug which could cause Auto-sklearn not to find a model on disk which is part of the ensemble.
FIX #961: Fixes a bug which caused Auto-sklearn to load bad meta-data for metrics which cannot be computed on multiclass datasets (especially ROC_AUC).
DOC #498: Improve the example on resampling strategies by showing how to pass scikit-learn's splitter objects to Auto-sklearn.
DOC #670: Demonstrate how to give access to training accuracy.
DOC #872: Improve an example on how obtain the best model.
DOC #940: Improve documentation of the docker image.
MAINT: Improve the docker file by setting environment variable that restrict BLAS and OMP to only use a single core.
MAINT #949: Replace pip by pip3 in the installation guidelines.
MAINT #280, #535, #956: Update meta-data and include regression meta-data again.

Contributors v0.10.0