All metrics except for AUC metric now use weights by default.
boost_from_averageparameter for RMSE training on CPU which might give a boost in quality.
Added conversion from ONNX to CatBoost. Now you can convert XGBoost or LightGBM model to ONNX, then convert it to CatBoost and use our fast applier. Use
model.load_model(model_path, format="onnx")for that.
Training is ~15% faster for datasets with categorical features.
NormalizedGini was not calculated, #962
Bug in leaf calculation which could result in slightly worse quality if you use weights in binary classification mode
__builtins__import in Python3 in PR #957, thanks to @AbhinavanT