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 (as the algorithm is exponential). Instead, many decision trees engines (including ML) try to find sub-optimal split in this case by clustering all the samples into MaxCategories clusters (i.e. some categories are merged together). Note that this technique is used only in N(>2)-class classification problems. 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.

public int MaxCategories { get; set; }

Public Property MaxCategories As Integer
	Get
	Set

public:
property int MaxCategories {
	int get ();
	void set (int value);
}

member MaxCategories : int with get, set