Catboost

Major Features And Improvements
- Python wrapper: added methods to download datasets titanic and amazon, to make it easier to try the library (`catboost.datasets`).
- Python wrapper: added method to write column desctiption file (`catboost.utils.create_cd`).
- Made improvements to visualization.
- Support non-numeric values in `GroupId` column.
- [Tutorials](https://github.com/catboost/tutorials/blob/master/README.md) section updated.

Bug Fixes and Other Changes
- Fixed problems with eval_metrics (issue 285)
- Other fixes

Breaking changes
- Changed parameter order in [`train()`](https://tech.yandex.com/catboost/doc/dg/concepts/python-reference_train-docpage/) 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`](https://tech.yandex.com/catboost/doc/dg/concepts/loss-functions-docpage/#loss-functions__ranking) 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](https://tech.yandex.com/catboost/doc/dg/concepts/python-reference_pool-docpage/)/[R-packages](https://tech.yandex.com/catboost/doc/dg/concepts/r-reference_catboost-load_pool-docpage/).
- 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.

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`](https://tech.yandex.com/catboost/doc/dg/concepts/python-reference_cv-docpage/).

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](https://tech.yandex.com/catboost/doc/dg/concepts/cli-installation-docpage/#make-install).
- 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](https://tech.yandex.com/catboost/doc/dg/concepts/cli-reference_eval-metrics-docpage/).
- New classification metric `CtrFactor` is [added](https://tech.yandex.com/catboost/doc/dg/concepts/loss-functions-docpage/).
- Load CatBoost model from memory. You can load your CatBoost model from file or initialize it from buffer [in memory](https://github.com/catboost/catboost/blob/master/catboost/CatboostModelAPI.md).
- 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](https://tech.yandex.com/catboost/doc/dg/concepts/output-data_training-log-docpage/).
- 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.

Major Features And Improvements
- **BETA** version of distributed mulit-host GPU via MPI training
- Added possibility to import coreml model with oblivious trees. Makes possible to migrate pre-flatbuffers model (with float features only) to current format (issue 235)
- Added QuerySoftMax loss function

Bug Fixes and Other Changes
- Fixed GPU models bug on pools with both categorical and float features (issue 241)
- Use all available cores by default
- Fixed not querywise loss for pool with `QueryId`
- Default float features binarization method set to `GreedyLogSum`

Bug Fixes and Other Changes
- Hotfix for critical bug in Python and R wrappers (issue 238)
- Added stratified data split in CV
- Fix `is_classification` check and CV for Logloss

Bug Fixes and Other Changes
- Fixed critical bugs in formula evaluation code (issue 236)
- Added scale_pos_weight parameter