org.opencv.ml

Class CvDTree

java.lang.Object

org.opencv.ml.CvStatModel

org.opencv.ml.CvDTree

public class CvDTree
extends CvStatModel

The class implements a decision tree as described in the beginning of this section.

See Also:

org.opencv.ml.CvDTree : public CvStatModel

Constructor Summary

Constructors

Constructor and Description
CvDTree()

Method Summary

Methods

Modifier and Type	Method and Description
void	clear()
Mat	getVarImportance() Returns the variable importance array.
boolean	train(Mat trainData, int tflag, Mat responses) Trains a decision tree.
boolean	train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvDTreeParams params) Trains a decision tree.

Methods inherited from class org.opencv.ml.CvStatModel

load, load, save, save

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

CvDTree

public CvDTree()

Method Detail

clear

public void clear()

getVarImportance

public Mat getVarImportance()

Returns the variable importance array.

See Also:

org.opencv.ml.CvDTree.getVarImportance

train

public boolean train(Mat trainData,
            int tflag,
            Mat responses)

Trains a decision tree.

There are four train methods in "CvDTree":

• The first two methods follow the generic "CvStatModel.train" conventions. It is the most complete form. Both data layouts (tflag=CV_ROW_SAMPLE and tflag=CV_COL_SAMPLE) are supported, as well as sample and variable subsets, missing measurements, arbitrary combinations of input and output variable types, and so on. The last parameter contains all of the necessary training parameters (see the "CvDTreeParams" description).
• The third method uses "CvMLData" to pass training data to a decision tree.
• The last method train is mostly used for building tree ensembles. It takes the pre-constructed "CvDTreeTrainData" instance and an optional subset of the training set. The indices in subsampleIdx are counted relatively to the _sample_idx, passed to the CvDTreeTrainData constructor. For example, if _sample_idx=[1, 5, 7, 100], then subsampleIdx=[0,3] means that the samples [1, 100] of the original training set are used.

The function is parallelized with the TBB library.

Parameters:

trainData - a trainData

tflag - a tflag

responses - a responses

See Also:

org.opencv.ml.CvDTree.train

train

public boolean train(Mat trainData,
            int tflag,
            Mat responses,
            Mat varIdx,
            Mat sampleIdx,
            Mat varType,
            Mat missingDataMask,
            CvDTreeParams params)

Trains a decision tree.

There are four train methods in "CvDTree":

• The first two methods follow the generic "CvStatModel.train" conventions. It is the most complete form. Both data layouts (tflag=CV_ROW_SAMPLE and tflag=CV_COL_SAMPLE) are supported, as well as sample and variable subsets, missing measurements, arbitrary combinations of input and output variable types, and so on. The last parameter contains all of the necessary training parameters (see the "CvDTreeParams" description).
• The third method uses "CvMLData" to pass training data to a decision tree.
• The last method train is mostly used for building tree ensembles. It takes the pre-constructed "CvDTreeTrainData" instance and an optional subset of the training set. The indices in subsampleIdx are counted relatively to the _sample_idx, passed to the CvDTreeTrainData constructor. For example, if _sample_idx=[1, 5, 7, 100], then subsampleIdx=[0,3] means that the samples [1, 100] of the original training set are used.

The function is parallelized with the TBB library.

Parameters:

trainData - a trainData

tflag - a tflag

responses - a responses

varIdx - a varIdx

sampleIdx - a sampleIdx

varType - a varType

missingDataMask - a missingDataMask

params - a params

See Also:

org.opencv.ml.CvDTree.train