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## weka.classifiers
Class IteratedSingleClassifierEnhancer

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.SingleClassifierEnhancer
              weka.classifiers.IteratedSingleClassifierEnhancer

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler

Direct Known Subclasses:
  AdditiveRegression, RandomizableIteratedSingleClassifierEnhancer

    public abstract class IteratedSingleClassifierEnhancer

extends SingleClassifierEnhancer

Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.

Version:
  $Revision: 1.4 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`IteratedSingleClassifierEnhancer()`

Method Summary
`void` | `buildClassifier(Instances data)` Stump method for building the classifiers.
`int` | `getNumIterations()` Gets the number of bagging iterations
`java.lang.String[]` | `getOptions()` Gets the current settings of the classifier.
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`java.lang.String` | `numIterationsTipText()` Returns the tip text for this property
`void` | `setNumIterations(int numIterations)` Sets the number of bagging iterations
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.

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

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

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

Constructor Detail

### IteratedSingleClassifierEnhancer

    public IteratedSingleClassifierEnhancer()

Method Detail

### buildClassifier

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

  Stump method for building the classifiers.

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `data` - the training data to be used for generating the bagged classifier.
  Throws:
    `java.lang.Exception` - if the classifier could not be built successfully

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `SingleClassifierEnhancer`
  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:

  -W classname
  Specify the full class name of the base learner.

  -I num
  Set the number of iterations (default 10).

  Options after -- are passed to the designated classifier.

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

### numIterationsTipText

    public java.lang.String numIterationsTipText()

  Returns the tip text for this property

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

### setNumIterations

    public void setNumIterations(int numIterations)

  Sets the number of bagging iterations

### getNumIterations

    public int getNumIterations()

  Gets the number of bagging iterations

  Returns:
    the maximum number of bagging iterations

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