MATLAB File Help: cv.Boost/MaxCategories Index
cv.Boost/MaxCategories

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.

Property Details
Constant false
Dependent true
Sealed false
Transient false
GetAccess 'public'
SetAccess 'public'
GetObservable false
SetObservable false