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OpenCV 2.4.3.1
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## org.opencv.ml
Class CvGBTrees

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

    public class CvGBTrees

extends CvStatModel

The class implements the Gradient boosted tree model as described in the beginning of this section.

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

Field Summary
`static int` | `ABSOLUTE_LOSS`
`static int` | `DEVIANCE_LOSS`
`static int` | `HUBER_LOSS`
`static int` | `SQUARED_LOSS`

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

Constructor Summary
`` | `CvGBTrees()` Default and training constructors.
`protected` | `CvGBTrees(long addr)`
`` | `CvGBTrees(Mat trainData, int tflag, Mat responses)` Default and training constructors.
`` | `CvGBTrees(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvGBTreesParams params)` Default and training constructors.

Method Summary
`void` | `clear()` Clears the model.
`protected void` | `finalize()`
`float` | `predict(Mat sample)` Predicts a response for an input sample.
`float` | `predict(Mat sample, Mat missing, Range slice, int k)` Predicts a response for an input sample.
`boolean` | `train(Mat trainData, int tflag, Mat responses)` Trains a Gradient boosted tree model.
`boolean` | `train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvGBTreesParams params, boolean update)` Trains a Gradient boosted tree 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`

Field Detail

### ABSOLUTE_LOSS

    public static final int ABSOLUTE_LOSS

See Also:
  Constant Field Values

### DEVIANCE_LOSS

    public static final int DEVIANCE_LOSS

See Also:
  Constant Field Values

### HUBER_LOSS

    public static final int HUBER_LOSS

See Also:
  Constant Field Values

### SQUARED_LOSS

    public static final int SQUARED_LOSS

See Also:
  Constant Field Values

Constructor Detail

### CvGBTrees

    public CvGBTrees()

  Default and training constructors.

  The constructors follow conventions of "CvStatModel.CvStatModel". See "CvStatModel.train" for parameters descriptions.

  See Also:
    org.opencv.ml.CvGBTrees.CvGBTrees

### CvGBTrees

    protected CvGBTrees(long addr)

### CvGBTrees

    public CvGBTrees(Mat trainData,
                     int tflag,
                     Mat responses)

  Default and training constructors.

  The constructors follow conventions of "CvStatModel.CvStatModel". See "CvStatModel.train" for parameters descriptions.

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

### CvGBTrees

    public CvGBTrees(Mat trainData,
                     int tflag,
                     Mat responses,
                     Mat varIdx,
                     Mat sampleIdx,
                     Mat varType,
                     Mat missingDataMask,
                     CvGBTreesParams params)

  Default and training constructors.

  The constructors follow conventions of "CvStatModel.CvStatModel". See "CvStatModel.train" for parameters descriptions.

  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.CvGBTrees.CvGBTrees

Method Detail

### clear

    public void clear()

  Clears the model.

  The function deletes the data set information and all the weak models and sets all internal variables to the initial state. The function is called in "CvGBTrees.train" and in the destructor.

  See Also:
    org.opencv.ml.CvGBTrees.clear

### finalize

    protected void finalize()
                     throws java.lang.Throwable

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

### predict

    public float predict(Mat sample)

  Predicts a response for an input sample.

  The method predicts the response corresponding to the given sample (see "Predicting with GBT"). The result is either the class label or the estimated function value. The "CvGBTrees.predict" method enables using the parallel version of the GBT model prediction if the OpenCV is built with the TBB library. In this case, predictions of single trees are computed in a parallel fashion.

  Parameters:
    `sample` - Input feature vector that has the same format as every training set element. If not all the variables were actually used during training, `sample` contains forged values at the appropriate places.
  See Also:
    org.opencv.ml.CvGBTrees.predict

### predict

    public float predict(Mat sample,
                         Mat missing,
                         Range slice,
                         int k)

  Predicts a response for an input sample.

  The method predicts the response corresponding to the given sample (see "Predicting with GBT"). The result is either the class label or the estimated function value. The "CvGBTrees.predict" method enables using the parallel version of the GBT model prediction if the OpenCV is built with the TBB library. In this case, predictions of single trees are computed in a parallel fashion.

  Parameters:
    `sample` - Input feature vector that has the same format as every training set element. If not all the variables were actually used during training, `sample` contains forged values at the appropriate places.
    `missing` - Missing values mask, which is a dimensional matrix of the same size as `sample` having the `CV_8U` type. `1` corresponds to the missing value in the same position in the `sample` vector. If there are no missing values in the feature vector, an empty matrix can be passed instead of the missing mask.
    `slice` - Parameter defining the part of the ensemble used for prediction.

    If `slice = Range.all()`, all trees are used. Use this parameter to get predictions of the GBT models with different ensemble sizes learning only one model.

    `k` - Number of tree ensembles built in case of the classification problem (see "Training GBT"). Use this parameter to change the output to sum of the trees' predictions in the `k`-th ensemble only. To get the total GBT model prediction, `k` value must be -1. For regression problems, `k` is also equal to -1.
  See Also:
    org.opencv.ml.CvGBTrees.predict

### train

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

  Trains a Gradient boosted tree model.

  The first train method follows the common template (see "CvStatModel.train"). Both `tflag` values (`CV_ROW_SAMPLE`, `CV_COL_SAMPLE`) are supported. `trainData` must be of the `CV_32F` type. `responses` must be a matrix of type `CV_32S` or `CV_32F`. In both cases it is converted into the `CV_32F` matrix inside the training procedure. `varIdx` and `sampleIdx` must be a list of indices (`CV_32S`) or a mask (`CV_8U` or `CV_8S`). `update` is a dummy parameter.

  The second form of "CvGBTrees.train" function uses "CvMLData" as a data set container. `update` is still a dummy parameter.

  All parameters specific to the GBT model are passed into the training function as a "CvGBTreesParams" structure.

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

### train

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

  Trains a Gradient boosted tree model.

  The first train method follows the common template (see "CvStatModel.train"). Both `tflag` values (`CV_ROW_SAMPLE`, `CV_COL_SAMPLE`) are supported. `trainData` must be of the `CV_32F` type. `responses` must be a matrix of type `CV_32S` or `CV_32F`. In both cases it is converted into the `CV_32F` matrix inside the training procedure. `varIdx` and `sampleIdx` must be a list of indices (`CV_32S`) or a mask (`CV_8U` or `CV_8S`). `update` is a dummy parameter.

  The second form of "CvGBTrees.train" function uses "CvMLData" as a data set container. `update` is still a dummy parameter.

  All parameters specific to the GBT model are passed into the training function as a "CvGBTreesParams" structure.

  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
    `update` - a update
  See Also:
    org.opencv.ml.CvGBTrees.train

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