CvDTree

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OpenCV 2.4.4

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org.opencv.ml
Class CvDTree

java.lang.Object
 extended by org.opencv.ml.CvStatModel
 extended by org.opencv.ml.CvDTree

public class CvDTree
extends CvStatModel
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
`CvDTree()`

Method Summary
`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