catboost/catboost v0.7

Release 0.7

on GitHub

Breaking changes

• Changed parameter order in train() function to be consistant with other GBDT libraries.
• use_best_model is set to True by default if eval_set labels are present.

Major Features And Improvements

• New ranking mode YetiRank optimizes NDGC and PFound.
• New visualisation for eval_metrics and cv in Jupyter notebook.
• Improved per document feature importance.
• Supported verbose=int: if verbose > 1, metric_period is set to this value.
• Supported type(eval_set) = list in python. Currently supporting only single eval_set.
• Binary classification leaf estimation defaults are changed for weighted datasets so that training converges for any weights.
• Add model_size_reg parameter to control model size. Fix ctr_leaf_count_limit parameter, also to control model size.
• Beta version of distributed CPU training with only float features support.
• Add subgroupId to Python/R-packages.
• Add groupwise metrics support in eval_metrics.

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.