Cluster possible values of a categorical variable into K <= MaxCategories clusters to find a suboptimal split.

If a discrete variable, on which the training procedure tries to make a split, takes more than MaxCategories values, the precise best subset estimation may take a very long time because the algorithm is exponential. Instead, many decision trees engines (including our implementation) try to find sub-optimal split in this case by clustering all the samples into MaxCategories clusters that is some categories are merged together. The clustering is applied only in n > 2-class classification problems for categorical variables with N > MaxCategories possible values. In case of regression and 2-class classification the optimal split can be found efficiently without employing clustering, thus the parameter is not used in these cases. Default value is 10.