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RTrees Properties

The RTrees type exposes the following members.

Properties
  NameDescription
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).
Protected propertyAllocatedMemory
Gets or sets a memory address allocated by AllocMemory.
(Inherited from DisposableObject.)
Protected propertyAllocatedMemorySize
Gets or sets the byte length of the allocated memory
(Inherited from DisposableObject.)
Public propertyCalculateVarImportance
If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. Default value is false.
Public propertyCVFolds
If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold cross-validation procedure where K is equal to CVFolds. Default value is 10.
(Inherited from DTrees.)
Public propertyCvPtr
Native pointer of OpenCV structure
(Inherited from DisposableCvObject.)
Protected propertyDataHandle
Gets or sets a handle which allocates using cvSetData.
(Inherited from DisposableObject.)
Public propertyEmpty
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
(Inherited from Algorithm.)
Public propertyIsDisposed
Gets a value indicating whether this instance has been disposed.
(Inherited from DisposableObject.)
Public propertyIsEnabledDispose
Gets or sets a value indicating whether you permit disposing this instance.
(Inherited from DisposableObject.)
Public propertyMaxCategories
Cluster possible values of a categorical variable into K < =maxCategories clusters to find a suboptimal split.
(Inherited from DTrees.)
Public propertyMaxDepth
The maximum possible depth of the tree.
(Inherited from DTrees.)
Public propertyMinSampleCount
If the number of samples in a node is less than this parameter then the node will not be split. Default value is 10.
(Inherited from DTrees.)
Public propertyPriors
The array of a priori class probabilities, sorted by the class label value.
(Inherited from DTrees.)
Public propertyRegressionAccuracy
Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split further. Default value is 0.01f.
(Inherited from DTrees.)
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. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree. Default value is true.
(Inherited from DTrees.)
Public propertyUse1SERule
If true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. Default value is true.
(Inherited from DTrees.)
Public propertyUseSurrogates
If true then surrogate splits will be built. These splits allow to work with missing data and compute variable importance correctly. Default value is false.
(Inherited from DTrees.)
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