catboost 0.16.3 on Python PyPI

Breaking changes:

Renamed column Feature Index to Feature Id in prettified output of python method get_feature_importance(), because it supports feature names now
Renamed option per_float_feature_binarization (--per-float-feature-binarization) to per_float_feature_quantization (--per-float-feature-quantization)
Removed parameter inverted from python cv method. Added type parameter instead, which can be set to Inverted
Method get_features() now works only for datasets without categorical features

A new multiclass version of AUC metric, called AUC Mu, which was proposed by Ross S. Kleiman on NeurIPS 2019, link
Added time series cv
Added MeanWeightedTarget in fstat
Added utils.get_confusion_matrix()
Now feature importance can be calculated for non-symmetric trees