catboost/catboost v0.6.3 on GitHub

Breaking changes

boosting_type parameter value Dynamic is renamed to Ordered.
Data visualisation functionality in Jupyter Notebook requires ipywidgets 7.x+ now.
query_id parameter renamed to group_id in Python and R wrappers.
cv returns pandas.DataFrame by default if Pandas installed. See new parameter as_pandas.

CatBoost build with make file. Now it’s possible to build command-line CPU version of CatBoost under Linux with make file.
In column description column name Target is changed to Label. It will still work with previous name, but it is recommended to use the new one.
eval-metrics mode added into cmdline version. Metrics can be calculated for a given dataset using a previously trained model.
New classification metric CtrFactor is added.
Load CatBoost model from memory. You can load your CatBoost model from file or initialize it from buffer in memory.
Now you can run fit function using file with dataset: fit(train_path, eval_set=eval_path, column_description=cd_file). This will reduce memory consumption by up to two times.
12% speedup for training.

JSON output data format is changed.
Python whl binaries with CUDA 9.1 support for Linux OS published into the release assets.
Added bootstrap_type parameter to CatBoostClassifier and Regressor (issue #263).

This release contains contributions from newbfg and CatBoost team.

We are grateful to all who filed issues or helped resolve them, asked and answered questions.