- cv is now stratified by default for
- We have removed
Loglossmetric. You need to use
target_borderas a separate training parameter now.
MultiClassif 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.
ostr_typeis renamed to
typein Python and R.
- Tree visualisation by @karina-usmanova.
- New feature analysis: plotting information about how a feature was used in the model by @alexrogozin12.
- 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.
- 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).
- 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.