github catboost/catboost v0.16

Breaking changes:

  • MultiClass loss has now the same sign as Logloss. It had the other sign before and was maximized, now it is minimized.
  • CatBoostRegressor.score now returns the value of R^2 metric instead of RMSE to be more consistent with the behavior of scikit-learn regressors.
  • Changed metric parameter use_weights default value to false (except for ranking metrics)

New features:

  • It is now possible to apply model on GPU
  • We have published two new realworld datasets with monotonic constraints, catboost.datasets.monotonic1() and catboost.datasets.monotonic2(). Before that there was only california_housing dataset in open-source with monotonic constraints. Now you can use these two to benchmark algorithms with monotonic constraints.
  • We've added several new metrics to catboost, including DCG, FairLoss, HammingLoss, NormalizedGini and FilteredNDCG
  • Introduced efficient GridSearch and RandomSearch implementations.
  • get_all_params() Python function returns the values of all training parameters, both user-defined and default.
  • Added more synonyms for training parameters to be more compatible with other GBDT libraries.


  • AUC metric is computationally very expensive. We've implemented parallelized calculation of this metric, now it can be calculated on every iteration (or every k-th iteration) about 4x faster.

Educational materials:

  • We've improved our command-line tutorial, now it has examples of files and more information.


  • Automatic Logloss or MultiClass loss function deduction for now also works if the training dataset is specified as Pool or filename string.
  • And some other fixes
latest releases: v1.0.0, v0.26.1, v0.26...
2 years ago