catboost v0.16 Release Notes

Release Date: 2019-07-24 // over 4 years ago
  • ๐Ÿ’ฅ 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 R2 metric instead of RMSE to be more consistent with the behavior of scikit-learn regressors.
    • ๐Ÿ”„ Changed metric parameter use_weights default value to false (except for ranking metrics)

    ๐Ÿ†• 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.
    • โž• Added more synonyms for training parameters to be more compatible with other GBDT libraries.

    Speedups:

    • 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.

    ๐Ÿ›  Fixes:

    • Automatic Logloss or MultiClass loss function deduction for CatBoostClassifier.fit now also works if the training dataset is specified as Pool or filename string.
    • ๐Ÿ›  And some other fixes