Update trunk/arcadia/catboost/github_toplevel/RELEASE.md
Minor fixes
# Release 0.7
-## 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
+## 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.
-## New functionality:
-- New ranking mode YetiRank optimizes NDGC and PFound.
+## 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.
- 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 `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
