catboost/catboost v0.23.2

0.23.2

on GitHub

New functionality

• Added plot_partial_dependence method in python-package (Now it works for models with symmetric trees trained on dataset with numerical features only). Implemented by @felixandrer.
• Allowed using boost_from_average option together with model_shrink_rate option. In this case shrinkage is applied to the starting value..
• Added new auto_class_weights option in python-package, R-package and cli with possible values Balanced and SqrtBalanced. For Balanced every class is weighted maxSumWeightInClass / sumWeightInClass, where sumWeightInClass is sum of weights of all samples in this class. If no weights are present then sample weight is 1. And maxSumWeightInClass - is maximum sum weight among all classes. For SqrtBalanced the formula is sqrt(maxSumWeightInClass / sumWeightInClass). This option supported in binclass and multiclass tasks. Implemented by @egiby.
• Supported model_size_reg option on GPU. Set to 0.5 by default (same as in CPU). This regularization works slightly differently on GPU: feature combinations are regularized more aggressively than on CPU. For CPU cost of a combination is equal to number of different feature values in this combinations that are present in training dataset. On GPU cost of a combination is equal to number of all possible different values of this combination. For example, if combination contains two categorical features c1 and c2, then the cost will be #categories in c1 * #categories in c2, even though many of the values from this combination might not be present in the dataset.
• Added calculation of Shapley values, (see formula (2) from https://arxiv.org/pdf/1802.03888.pdf). By default estimation from this paper (Algorithm 2) is calcucated, that is much more faster. To use this mode specify shap_calc_type parameter of CatBoost.get_feature_importance function as "Exact". Implemented by @LordProtoss.

Bugfixes:

• Fixed onnx converter for old onnx versions.