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catboost v0.15 Release Notes

Release Date: 2019-05-27 // almost 5 years ago

  • 💥 Breaking changes

    • 0️⃣ 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.