github catboost/catboost v0.6.3
Release 0.6.3

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.

Major Features And Improvements

  • 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.

Bug Fixes and Other Changes

  • 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).

Thanks to our Contributors

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.

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