MultiClassloss has now the same sign as Logloss. It had the other sign before and was maximized, now it is minimized.
CatBoostRegressor.scorenow 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_weightsdefault value to false (except for ranking metrics)
- It is now possible to apply model on GPU
- We have published two new realworld datasets with monotonic constraints,
catboost.datasets.monotonic2(). Before that there was only
california_housingdataset 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
- Introduced efficient
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.
- We've improved our command-line tutorial, now it has examples of files and more information.
MultiClassloss function deduction for
CatBoostClassifier.fitnow also works if the training dataset is specified as
Poolor filename string.
- And some other fixes