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## weka.classifiers.meta
Class AdaBoostM1

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.SingleClassifierEnhancer
              weka.classifiers.IteratedSingleClassifierEnhancer
                  weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                      weka.classifiers.meta.AdaBoostM1

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Sourcable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

Direct Known Subclasses:
  MultiBoostAB

    public class AdaBoostM1

extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, Sourcable, TechnicalInformationHandler

Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves performance, but sometimes overfits.
For more information, see
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

BibTeX:

     @inproceedings{Freund1996,
        address = {San Francisco},
        author = {Yoav Freund and Robert E. Schapire},
        booktitle = {Thirteenth International Conference on Machine Learning},
        pages = {148-156},
        publisher = {Morgan Kaufmann},
        title = {Experiments with a new boosting algorithm},
        year = {1996}
     }

Valid options are:

     -P <num>
      Percentage of weight mass to base training on.
      (default 100, reduce to around 90 speed up)

     -Q
      Use resampling for boosting.

     -S <num>
      Random number seed.
      (default 1)

     -I <num>
      Number of iterations.
      (default 10)

     -D
      If set, classifier is run in debug mode and
      may output additional info to the console

     -W
      Full name of base classifier.
      (default: weka.classifiers.trees.DecisionStump)

     Options specific to classifier weka.classifiers.trees.DecisionStump:

     -D
      If set, classifier is run in debug mode and
      may output additional info to the console

Options after -- are passed to the designated classifier.

Version:
  $Revision: 1.40 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`AdaBoostM1()` Constructor.

Method Summary
`void` | `buildClassifier(Instances data)` Boosting method.
`double[]` | `distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`TechnicalInformation` | `getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`boolean` | `getUseResampling()` Get whether resampling is turned on
`int` | `getWeightThreshold()` Get the degree of weight thresholding
`java.lang.String` | `globalInfo()` Returns a string describing classifier
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setUseResampling(boolean r)` Set resampling mode
`void` | `setWeightThreshold(int threshold)` Set weight threshold
`java.lang.String` | `toSource(java.lang.String className)` Returns the boosted model as Java source code.
`java.lang.String` | `toString()` Returns description of the boosted classifier.
`java.lang.String` | `useResamplingTipText()` Returns the tip text for this property
`java.lang.String` | `weightThresholdTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
`getSeed, seedTipText, setSeed`

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
`getNumIterations, numIterationsTipText, setNumIterations`

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
`classifierTipText, getClassifier, setClassifier`

Methods inherited from class weka.classifiers.Classifier
`classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

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

Constructor Detail

### AdaBoostM1

    public AdaBoostM1()

  Constructor.

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing classifier

  Returns:
    a description suitable for displaying in the explorer/experimenter gui

### getTechnicalInformation

    public TechnicalInformation getTechnicalInformation()

  Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

  Specified by:
    `getTechnicalInformation` in interface `TechnicalInformationHandler`
  Returns:
    the technical information about this class

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `RandomizableIteratedSingleClassifierEnhancer`
  Returns:
    an enumeration of all the available options.

### setOptions

    public void setOptions(java.lang.String[] options)
                    throws java.lang.Exception

  Parses a given list of options.

  Valid options are:

       -P <num>
        Percentage of weight mass to base training on.
        (default 100, reduce to around 90 speed up)

       -Q
        Use resampling for boosting.

       -S <num>
        Random number seed.
        (default 1)

       -I <num>
        Number of iterations.
        (default 10)

       -D
        If set, classifier is run in debug mode and
        may output additional info to the console

       -W
        Full name of base classifier.
        (default: weka.classifiers.trees.DecisionStump)

       Options specific to classifier weka.classifiers.trees.DecisionStump:

       -D
        If set, classifier is run in debug mode and
        may output additional info to the console

  Options after -- are passed to the designated classifier.

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `RandomizableIteratedSingleClassifierEnhancer`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an option is not supported

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the Classifier.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `RandomizableIteratedSingleClassifierEnhancer`
  Returns:
    an array of strings suitable for passing to setOptions

### weightThresholdTipText

    public java.lang.String weightThresholdTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setWeightThreshold

    public void setWeightThreshold(int threshold)

  Set weight threshold

  Parameters:
    `threshold` - the percentage of weight mass used for training

### getWeightThreshold

    public int getWeightThreshold()

  Get the degree of weight thresholding

  Returns:
    the percentage of weight mass used for training

### useResamplingTipText

    public java.lang.String useResamplingTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setUseResampling

    public void setUseResampling(boolean r)

  Set resampling mode

  Parameters:
    `r` - true if resampling should be done

### getUseResampling

    public boolean getUseResampling()

  Get whether resampling is turned on

  Returns:
    true if resampling output is on

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `SingleClassifierEnhancer`
  Returns:
    the capabilities of this classifier
  See Also:
    `Capabilities`

### buildClassifier

    public void buildClassifier(Instances data)
                         throws java.lang.Exception

  Boosting method.

  Overrides:
    `buildClassifier` in class `IteratedSingleClassifierEnhancer`
  Parameters:
    `data` - the training data to be used for generating the boosted classifier.
  Throws:
    `java.lang.Exception` - if the classifier could not be built successfully

### distributionForInstance

    public double[] distributionForInstance(Instance instance)
                                     throws java.lang.Exception

  Calculates the class membership probabilities for the given test instance.

  Overrides:
    `distributionForInstance` in class `Classifier`
  Parameters:
    `instance` - the instance to be classified
  Returns:
    predicted class probability distribution
  Throws:
    `java.lang.Exception` - if instance could not be classified successfully

### toSource

    public java.lang.String toSource(java.lang.String className)
                              throws java.lang.Exception

  Returns the boosted model as Java source code.

  Specified by:
    `toSource` in interface `Sourcable`
  Parameters:
    `className` - the classname of the generated class
  Returns:
    the tree as Java source code
  Throws:
    `java.lang.Exception` - if something goes wrong

### toString

    public java.lang.String toString()

  Returns description of the boosted classifier.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    description of the boosted classifier as a string

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `Classifier`
  Returns:
    the revision

### main

    public static void main(java.lang.String[] argv)

  Main method for testing this class.

  Parameters:
    `argv` - the options

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