github catboost/catboost v0.15

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

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.

latest releases: v1.0.0, v0.26.1, v0.26...
2 years ago