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OpenCV 2.4.3.1
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## org.opencv.ml
Class CvRTrees

    java.lang.Object
      org.opencv.ml.CvStatModel
          org.opencv.ml.CvRTrees

Direct Known Subclasses:
  CvERTrees

    public class CvRTrees

extends CvStatModel

The class implements the random forest predictor as described in the beginning of this section.

See Also:
  org.opencv.ml.CvRTrees : public CvStatModel

Field Summary

Fields inherited from class org.opencv.ml.CvStatModel
`nativeObj`

Constructor Summary
`` | `CvRTrees()`
`protected` | `CvRTrees(long addr)`

Method Summary
`void` | `clear()`
`protected void` | `finalize()`
`Mat` | `getVarImportance()` Returns the variable importance array.
`float` | `predict_prob(Mat sample)` Returns a fuzzy-predicted class label.
`float` | `predict_prob(Mat sample, Mat missing)` Returns a fuzzy-predicted class label.
`float` | `predict(Mat sample)` Predicts the output for an input sample.
`float` | `predict(Mat sample, Mat missing)` Predicts the output for an input sample.
`boolean` | `train(Mat trainData, int tflag, Mat responses)` Trains the Random Trees model.
`boolean` | `train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvRTParams params)` Trains the Random Trees model.

Methods inherited from class org.opencv.ml.CvStatModel
`load, load, save, save`

Methods inherited from class java.lang.Object
`clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### CvRTrees

    public CvRTrees()

### CvRTrees

    protected CvRTrees(long addr)

Method Detail

### clear

    public void clear()

### finalize

    protected void finalize()
                     throws java.lang.Throwable

  Overrides:
    `finalize` in class `CvStatModel`
  Throws:
    `java.lang.Throwable`

### getVarImportance

    public Mat getVarImportance()

  Returns the variable importance array.

  The method returns the variable importance vector, computed at the training stage when `CvRTParams.calc_var_importance` is set to true. If this flag was set to false, the `NULL` pointer is returned. This differs from the decision trees where variable importance can be computed anytime after the training.

  See Also:
    org.opencv.ml.CvRTrees.getVarImportance

### predict_prob

    public float predict_prob(Mat sample)

  Returns a fuzzy-predicted class label.

  The function works for binary classification problems only. It returns the number between 0 and 1. This number represents probability or confidence of the sample belonging to the second class. It is calculated as the proportion of decision trees that classified the sample to the second class.

  Parameters:
    `sample` - Sample for classification.
  See Also:
    org.opencv.ml.CvRTrees.predict_prob

### predict_prob

    public float predict_prob(Mat sample,
                              Mat missing)

  Returns a fuzzy-predicted class label.

  The function works for binary classification problems only. It returns the number between 0 and 1. This number represents probability or confidence of the sample belonging to the second class. It is calculated as the proportion of decision trees that classified the sample to the second class.

  Parameters:
    `sample` - Sample for classification.
    `missing` - Optional missing measurement mask of the sample.
  See Also:
    org.opencv.ml.CvRTrees.predict_prob

### predict

    public float predict(Mat sample)

  Predicts the output for an input sample.

  The input parameters of the prediction method are the same as in "CvDTree.predict" but the return value type is different. This method returns the cumulative result from all the trees in the forest (the class that receives the majority of voices, or the mean of the regression function estimates).

  Parameters:
    `sample` - Sample for classification.
  See Also:
    org.opencv.ml.CvRTrees.predict

### predict

    public float predict(Mat sample,
                         Mat missing)

  Predicts the output for an input sample.

  The input parameters of the prediction method are the same as in "CvDTree.predict" but the return value type is different. This method returns the cumulative result from all the trees in the forest (the class that receives the majority of voices, or the mean of the regression function estimates).

  Parameters:
    `sample` - Sample for classification.
    `missing` - Optional missing measurement mask of the sample.
  See Also:
    org.opencv.ml.CvRTrees.predict

### train

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

  Trains the Random Trees model.

  The method "CvRTrees.train" is very similar to the method "CvDTree.train" and follows the generic method "CvStatModel.train" conventions. All the parameters specific to the algorithm training are passed as a "CvRTParams" instance. The estimate of the training error (`oob-error`) is stored in the protected class member `oob_error`.

  The function is parallelized with the TBB library.

  Parameters:
    `trainData` - a trainData
    `tflag` - a tflag
    `responses` - a responses
  See Also:
    org.opencv.ml.CvRTrees.train

### train

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

  Trains the Random Trees model.

  The method "CvRTrees.train" is very similar to the method "CvDTree.train" and follows the generic method "CvStatModel.train" conventions. All the parameters specific to the algorithm training are passed as a "CvRTParams" instance. The estimate of the training error (`oob-error`) is stored in the protected class member `oob_error`.

  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.CvRTrees.train

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