The size of the randomly selected subset of features at each tree node and that are used to find the best split(s)

If true then variable importance will be calculated

If CVFolds greater than 1 then algorithms prunes the built decision tree using K-fold

Cluster possible values of a categorical variable into K less than or equals maxCategories clusters to find a suboptimal split

The maximum possible depth of the tree

If the number of samples in a node is less than this parameter then the node will not be split

Pointer to the unmanaged object

Termination criteria for regression trees

The termination criteria that specifies when the training algorithm stops

If true then pruned branches are physically removed from the tree

If true then a pruning will be harsher

If true then surrogate splits will be built