- Changed parameter order in
train()function to be consistant with other GBDT libraries.
use_best_modelis set to True by default if
eval_setlabels are present.
Major Features And Improvements
- New ranking mode
- New visualisation for
cvin Jupyter notebook.
- Improved per document feature importance.
metric_periodis set to this value.
- Supported type(
eval_set) = list in python. Currently supporting only single
- Binary classification leaf estimation defaults are changed for weighted datasets so that training converges for any weights.
model_size_regparameter to control model size. Fix
ctr_leaf_count_limitparameter, also to control model size.
- Beta version of distributed CPU training with only float features support.
- Add groupwise metrics support in
Thanks to our Contributors
This release contains contributions from CatBoost team.
We are grateful to all who filed issues or helped resolve them, asked and answered questions.