catboost 0.18

v0.18

on Python PyPI

• The main feature of the release is huge speedup on small datasets. We now use MVS sampling for CPU regression and binary classification training by default, together with Plain boosting scheme for both small and large datasets. This change not only gives the huge speedup but also provides quality improvement!
• The boost_from_average parameter is available in CatBoostClassifier and CatBoostRegressor
• We have added new formats for describing monotonic constraints. For example, "(1,0,0,-1)" or "0:1,3:-1" or "FeatureName0:1,FeatureName3:-1" are all valid specifications. With Python and params-file json, lists and dictionaries can also be used

Bugs fixed:

• Error in Multiclass classifier training, #1040
• Unhandled exception when saving quantized pool, #1021
• Python 3.7: RuntimeError raised in StagedPredictIterator, #848