SHAP values tutorial link update
- Algorithm for finding most influential training samples for a given object from the 'Finding Influential Training Samples for Gradient Boosted Decision Trees' [paper](https://arxiv.org/pdf/1802.06640.pdf) is implemented. This mode for every object from input pool calculates scores for every object from train pool. A positive score means that the given train object has made a negative contribution to the given test object prediction. And vice versa for negative scores. The higher score modulo - the higher contribution.
See `get_object_importance` model method in Python package and `ostr` mode in cli-version. Tutorial for Python is available [here](https://github.com/catboost/catboost/blob/master/catboost/tutorials/advanced_tutorials/catboost_object_importance_tutorial.ipynb).
More details and examples will be published in documentation soon.
-- We have implemented new way of exploring feature importance - Shap values from [paper](https://arxiv.org/pdf/1706.06060.pdf). This allows to understand which features are most influent for a given object. You can also get more insite about your model, see details in a [tutorial](https://github.com/catboost/catboost/blob/master/catboost/tutorials/advanced_tutorials/catboost_shap_tutorial.ipynb).
+- We have implemented new way of exploring feature importance - Shap values from [paper](https://arxiv.org/pdf/1706.06060.pdf). This allows to understand which features are most influent for a given object. You can also get more insite about your model, see details in a [tutorial](https://github.com/catboost/catboost/blob/master/catboost/tutorials/advanced_tutorials/shap_values_tutorial.ipynb).
- Save model as code functionality published. For now you could save model as Python code with categorical features and as C++ code w/o categorical features.
## Bug Fixes and Other Changes
