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()
andcatboost.datasets.monotonic2()
. Before that there was onlycalifornia_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
andFilteredNDCG
- Introduced efficient
GridSearch
andRandomSearch
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
orMultiClass
loss function deduction forCatBoostClassifier.fit
now also works if the training dataset is specified asPool
or filename string. - And some other fixes