An up-to-date list of available CatBoost releases and the corresponding binaries for different operating systems is available in the Download section of the releases page on GitHub.

Contains A vector with contributions of each feature to the prediction for every input object and the expected value of the model prediction for the object (average prediction given no knowledge about the object). Use the SHAP package to plot the returned values. Format The rows are sorted in the same order as the order of objects in the input dataset. Each row contains information related to one object from the input dataset. Format:<contribution of feature 1><\t><contribution of feature 2><\t> .. <\t><contribution of feature N><\t><expected value of the model prediction> Example -0.0001401524197<\t>0.0001269417313<\t>0.004920700379<\t>0,00490749

Calculate and return the feature importances.