catboost/catboost v0.16.5

on GitHub

Breaking changes:

• All metrics except for AUC metric now use weights by default.

New features:

• Added boost_from_average parameter 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.

Speed ups:

• Training is ~15% faster for datasets with categorical features.

Bug fixes:

• R language: get_features_importance with ShapValues for MultiClass, #868
• NormalizedGini was not calculated, #962
• Bug in leaf calculation which could result in slightly worse quality if you use weights in binary classification mode
• Fixed __builtins__ import in Python3 in PR #957, thanks to @AbhinavanT