github catboost/catboost v0.21
0.21

New features:

  • The main feature of this release is the Stochastic Gradient Langevin Boosting (SGLB) mode that can improve quality of your models with non-convex loss functions. To use it specify langevin option and tune diffusion_temperature and model_shrink_rate. See the corresponding paper for details.

Improvements:

  • Automatic learning rate is applied by default not only for Logloss objective, but also for RMSE (on CPU and GPU) and MultiClass (on GPU).
  • Class labels type information is stored in the model. Now estimators in python package return values of proper type in classes_ attribute and for prediction functions with prediction_type=Class. #305, #999, #1017.
    Note: Class labels loaded from datasets in CatBoost dsv format always have string type now.

Bug fixes:

  • Fixed huge memory consumption for text features. #1107
  • Fixed crash on GPU on big datasets with groups (hundred million+ groups).
  • Fixed class labels consistency check and merging in model sums (now class names in binary classification are properly checked and added to the result as well)
  • Fix for confusion matrix (PR #1152), thanks to @dmsivkov.
  • Fixed shap values calculation when boost_from_average=True. #1125
  • Fixed use-after-free in fstr PredictionValuesChange with specified dataset
  • Target border and class weights are now taken from model when necessary for feature strength, metrics evaluation, roc_curve, object importances and calc_feature_statistics calculations.
  • Fixed that L2 regularization was not applied for non symmetric trees for binary classification on GPU.
  • [R-package] Fixed the bug that catboost.get_feature_importance did not work after model is loaded #1064
  • [R-package] Fixed the bug that catboost.train did not work when called with the single dataset parameter. #1162
  • Fixed L2 score calculation on CPU

Other:

  • Starting from this release Java applier is released simultaneously with other components and has the same version.

Compatibility:

  • Models trained with this release require applier from this release or later to work correctly.
latest releases: v1.0.0, v0.26.1, v0.26...
21 months ago