public CvBoostParams(
	BoostType boostType,
	int weakCount,
	double weightTrimRate,
	int maxDepth,
	bool useSurrogates,
	float[] priors
)

Public Sub New ( 
	boostType As BoostType,
	weakCount As Integer,
	weightTrimRate As Double,
	maxDepth As Integer,
	useSurrogates As Boolean,
	priors As Single()
)

public:
CvBoostParams(
	BoostType boostType, 
	int weakCount, 
	double weightTrimRate, 
	int maxDepth, 
	bool useSurrogates, 
	array<float>^ priors
)

new : 
        boostType : BoostType * 
        weakCount : int * 
        weightTrimRate : float * 
        maxDepth : int * 
        useSurrogates : bool * 
        priors : float32[] -> CvBoostParams

Parameters

boostType

Type: OpenCvSharp.CPlusPlusBoostType
Boosting type

weakCount

Type: SystemInt32
The number of weak classifiers to build.

weightTrimRate

Type: SystemDouble
he weight trimming ratio, within 0..1. If the parameter is ≤0 or >1, the trimming is not used, all the samples are used at each iteration. The default value is 0.95.

maxDepth

Type: SystemInt32
This parameter specifies the maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than max_depth. The actual depth may be smaller if the other termination criteria are met (see the outline of the training procedure in the beginning of the section), and/or if the tree is pruned.

useSurrogates

Type: SystemBoolean
If true, surrogate splits are built. Surrogate splits are needed to handle missing measurements and for variable importance estimation.

priors

Type: SystemSingle
The array of a priori class probabilities, sorted by the class label value. The parameter can be used to tune the decision tree preferences toward a certain class. For example, if users want to detect some rare anomaly occurrence, the training base will likely contain much more normal cases than anomalies, so a very good classification performance will be achieved just by considering every case as normal. To avoid this, the priors can be specified, where the anomaly probability is artificially increased (up to 0.5 or even greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is adjusted properly.

Reference