github catboost/catboost v0.16.5

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

latest releases: v1.0.0, v0.26.1, v0.26...
2 years ago