Click or drag to resize

RTrees Properties

The RTrees type exposes the following members.

Public propertyActiveVarCount
The size of the randomly selected subset of features at each tree node and that are used to find the best split(s)
Public propertyCalculateVarImportance
If true then variable importance will be calculated
Public propertyCVFolds
If CVFolds greater than 1 then algorithms prunes the built decision tree using K-fold
Public propertyMaxCategories
Cluster possible values of a categorical variable into K less than or equals maxCategories clusters to find a suboptimal split
Public propertyMaxDepth
The maximum possible depth of the tree
Public propertyMinSampleCount
If the number of samples in a node is less than this parameter then the node will not be split
Public propertyPtr
Pointer to the unmanaged object
(Inherited from UnmanagedObject.)
Public propertyRegressionAccuracy
Termination criteria for regression trees
Public propertyTermCriteria
The termination criteria that specifies when the training algorithm stops
Public propertyTruncatePrunedTree
If true then pruned branches are physically removed from the tree
Public propertyUse1SERule
If true then a pruning will be harsher
Public propertyUseSurrogates
If true then surrogate splits will be built
See Also