catboost 0.16

v0.16

on Python PyPI

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.

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.

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.
• And some other fixes