« Changelog History


catboost v0.16.5 Release Notes

Release Date: 2019-08-20 // over 4 years ago

  • 💥 Breaking changes:

    • 0️⃣ 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