catboost/catboost v0.15

on GitHub

Breaking changes

• cv is now stratified by default for Logloss, MultiClass and MultiClassOneVsAll.
• We have removed border parameter of Logloss metric. You need to use target_border as a separate training parameter now.
• CatBoostClassifier now runs MultiClass if more than 2 different values are present in training dataset labels.
• model.best_score_["validation_0"] is replaced with model.best_score_["validation"] if a single validation dataset is present.
• get_object_importance function parameter ostr_type is renamed to type in Python and R.

Model analysis

• Tree visualisation by @karina-usmanova.
• New feature analysis: plotting information about how a feature was used in the model by @alexrogozin12.
• Added plot parameter to get_roc_curve, get_fpr_curve and get_fnr_curve functions from catboost.utils.
• Supported prettified format for all types of feature importances.

New ways of doing predictions

• Rust applier by @shuternay.
• DotNet applier by @17minutes.
• One-hot encoding for categorical features in CatBoost CoreML model by Kseniya Valchuk and Ekaterina Pogodina.

New objectives

• Expectile Regression by @david-waterworth.
• Huber loss by @atsky.

Speedups

• Speed up of shap values calculation for single object or for small number of objects by @Lokutrus.
• Cheap preprocessing and no fighting of overfitting if there is little amount of iterations (since you will not overfit anyway).

New functionality

• Prediction of leaf indices.

New educational materials

• Rust tutorial by @shuternay.
• C# tutorial.
• Leaf indices.
• Tree visualisation tutorial by @karina-usmanova.
• Google Colab tutorial for regression in catboost by @col14m.

And a set of fixes for your issues.