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OpenCV 2.4.3.1
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## org.opencv.ml
Class CvBoost

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

    public class CvBoost

extends CvStatModel

Boosted tree classifier derived from "CvStatModel".

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

Field Summary
`static int` | `DEFAULT`
`static int` | `DISCRETE`
`static int` | `GENTLE`
`static int` | `GINI`
`static int` | `LOGIT`
`static int` | `MISCLASS`
`static int` | `REAL`
`static int` | `SQERR`

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

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

Method Summary
`void` | `clear()`
`protected void` | `finalize()`
`float` | `predict(Mat sample)` Predicts a response for an input sample.
`float` | `predict(Mat sample, Mat missing, Range slice, boolean rawMode, boolean returnSum)` Predicts a response for an input sample.
`void` | `prune(Range slice)` Removes the specified weak classifiers.
`boolean` | `train(Mat trainData, int tflag, Mat responses)` Trains a boosted tree classifier.
`boolean` | `train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvBoostParams params, boolean update)` Trains a boosted tree classifier.

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

### DEFAULT

    public static final int DEFAULT

See Also:
  Constant Field Values

### DISCRETE

    public static final int DISCRETE

See Also:
  Constant Field Values

### GENTLE

    public static final int GENTLE

See Also:
  Constant Field Values

### GINI

    public static final int GINI

See Also:
  Constant Field Values

### LOGIT

    public static final int LOGIT

See Also:
  Constant Field Values

### MISCLASS

    public static final int MISCLASS

See Also:
  Constant Field Values

### REAL

    public static final int REAL

See Also:
  Constant Field Values

### SQERR

    public static final int SQERR

See Also:
  Constant Field Values

Constructor Detail

### CvBoost

    public CvBoost()

  Default and training constructors.

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

  See Also:
    org.opencv.ml.CvBoost.CvBoost

### CvBoost

    protected CvBoost(long addr)

### CvBoost

    public CvBoost(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.CvBoost.CvBoost

### CvBoost

    public CvBoost(Mat trainData,
                   int tflag,
                   Mat responses,
                   Mat varIdx,
                   Mat sampleIdx,
                   Mat varType,
                   Mat missingDataMask,
                   CvBoostParams 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.CvBoost.CvBoost

Method Detail

### clear

    public void 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 runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting.

  Parameters:
    `sample` - Input sample.
  See Also:
    org.opencv.ml.CvBoost.predict

### predict

    public float predict(Mat sample,
                         Mat missing,
                         Range slice,
                         boolean rawMode,
                         boolean returnSum)

  Predicts a response for an input sample.

  The method runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting.

  Parameters:
    `sample` - Input sample.
    `missing` - Optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits (see `CvDTreeParams.use_surrogates`).
    `slice` - Continuous subset of the sequence of weak classifiers to be used for prediction. By default, all the weak classifiers are used.
    `rawMode` - a rawMode
    `returnSum` - a returnSum
  See Also:
    org.opencv.ml.CvBoost.predict

### prune

    public void prune(Range slice)

  Removes the specified weak classifiers.

  The method removes the specified weak classifiers from the sequence.

  Note: Do not confuse this method with the pruning of individual decision trees, which is currently not supported.

  Parameters:
    `slice` - Continuous subset of the sequence of weak classifiers to be removed.
  See Also:
    org.opencv.ml.CvBoost.prune

### train

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

  Trains a boosted tree classifier.

  The train method follows the common template of "CvStatModel.train". The responses must be categorical, which means that boosted trees cannot be built for regression, and there should be two classes.

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

### train

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

  Trains a boosted tree classifier.

  The train method follows the common template of "CvStatModel.train". The responses must be categorical, which means that boosted trees cannot be built for regression, and there should be two classes.

  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` - Specifies whether the classifier needs to be updated (`true`, the new weak tree classifiers added to the existing ensemble) or the classifier needs to be rebuilt from scratch (`false`).
  See Also:
    org.opencv.ml.CvBoost.train

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