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The method takes the feature vector and the optional missing measurement mask on input, traverses the decision tree and returns the reached leaf node on output. The prediction result, either the class label or the estimated function value, may be retrieved as value field of the CvDTreeNode structure

Namespace: Emgu.CV.ML
Assembly: Emgu.CV.ML (in Emgu.CV.ML.dll) Version: 2.4.0.1717 (2.4.0.1717)

Syntax

C#
public static IntPtr CvDTreePredict(
	IntPtr model,
	IntPtr sample,
	IntPtr missingDataMask,
	bool rawMode
)
Visual Basic
Public Shared Function CvDTreePredict ( _
	model As IntPtr, _
	sample As IntPtr, _
	missingDataMask As IntPtr, _
	rawMode As Boolean _
) As IntPtr
Visual C++
public:
static IntPtr CvDTreePredict(
	IntPtr model, 
	IntPtr sample, 
	IntPtr missingDataMask, 
	bool rawMode
)

Parameters

model
Type: System..::..IntPtr
The decision tree model
sample
Type: System..::..IntPtr
The sample to be predicted
missingDataMask
Type: System..::..IntPtr
Can be IntPtr.Zero if not needed. When specified, it is an 8-bit matrix of the same size as trainData, is used to mark the missed values (non-zero elements of the mask)
rawMode
Type: System..::..Boolean
Normally set to false that implies a regular input. If it is true, the method assumes that all the values of the discrete input variables have been already normalized to 0..num_of_categoriesi-1 ranges. (as the decision tree uses such normalized representation internally). It is useful for faster prediction with tree ensembles. For ordered input variables the flag is not used.

Return Value

Pointer to the reached leaf node on output. The prediction result, either the class label or the estimated function value, may be retrieved as value field of the CvDTreeNode structure

See Also