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## weka.classifiers.meta
Class Dagging

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.SingleClassifierEnhancer
              weka.classifiers.RandomizableSingleClassifierEnhancer
                  weka.classifiers.meta.Dagging

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

    public class Dagging

extends RandomizableSingleClassifierEnhancer
implements TechnicalInformationHandler

This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier.
Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data.
For more information, see:
Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.

BibTeX:

     @inproceedings{Ting1997,
        address = {San Francisco, CA},
        author = {Ting, K. M. and Witten, I. H.},
        booktitle = {Fourteenth international Conference on Machine Learning},
        editor = {D. H. Fisher},
        pages = {367-375},
        publisher = {Morgan Kaufmann Publishers},
        title = {Stacking Bagged and Dagged Models},
        year = {1997}
     }

Valid options are:

     -F <folds>
      The number of folds for splitting the training set into
      smaller chunks for the base classifier.
      (default 10)

     -verbose
      Whether to print some more information during building the
      classifier.
      (default is off)

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

     -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.functions.SMO)

     Options specific to classifier weka.classifiers.functions.SMO:

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

     -no-checks
      Turns off all checks - use with caution!
      Turning them off assumes that data is purely numeric, doesn't
      contain any missing values, and has a nominal class. Turning them
      off also means that no header information will be stored if the
      machine is linear. Finally, it also assumes that no instance has
      a weight equal to 0.
      (default: checks on)

     -C <double>
      The complexity constant C. (default 1)

     -N
      Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)

     -L <double>
      The tolerance parameter. (default 1.0e-3)

     -P <double>
      The epsilon for round-off error. (default 1.0e-12)

     -M
      Fit logistic models to SVM outputs.

     -V <double>
      The number of folds for the internal
      cross-validation. (default -1, use training data)

     -W <double>
      The random number seed. (default 1)

     -K <classname and parameters>
      The Kernel to use.
      (default: weka.classifiers.functions.supportVector.PolyKernel)

     Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:

     -D
      Enables debugging output (if available) to be printed.
      (default: off)

     -no-checks
      Turns off all checks - use with caution!
      (default: checks on)

     -C <num>
      The size of the cache (a prime number), 0 for full cache and
      -1 to turn it off.
      (default: 250007)

     -E <num>
      The Exponent to use.
      (default: 1.0)

     -L
      Use lower-order terms.
      (default: no)

Options after -- are passed to the designated classifier.

Version:
  $Revision: 5928 $
Author:
  Bernhard Pfahringer (bernhard at cs dot waikato dot ac dot nz), FracPete (fracpete at waikato dot ac dot nz)
See Also:
  `Vote`, Serialized Form

Constructor Summary
`Dagging()` Constructor.

Method Summary
`void` | `buildClassifier(Instances data)` Bagging method.
`double[]` | `distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`int` | `getNumFolds()` Gets the number of folds to use for splitting the training set.
`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` | `getVerbose()` Gets the verbose state
`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[] args)` Main method for testing this class.
`java.lang.String` | `numFoldsTipText()` Returns the tip text for this property
`void` | `setNumFolds(int value)` Sets the number of folds to use for splitting the training set.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setVerbose(boolean value)` Set the verbose state.
`java.lang.String` | `toString()` Returns description of the classifier.
`java.lang.String` | `verboseTipText()` Returns the tip text for this property

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

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

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

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

Constructor Detail

### Dagging

    public Dagging()

  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 `RandomizableSingleClassifierEnhancer`
  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:

       -F <folds>
        The number of folds for splitting the training set into
        smaller chunks for the base classifier.
        (default 10)

       -verbose
        Whether to print some more information during building the
        classifier.
        (default is off)

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

       -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.functions.SMO)

       Options specific to classifier weka.classifiers.functions.SMO:

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

       -no-checks
        Turns off all checks - use with caution!
        Turning them off assumes that data is purely numeric, doesn't
        contain any missing values, and has a nominal class. Turning them
        off also means that no header information will be stored if the
        machine is linear. Finally, it also assumes that no instance has
        a weight equal to 0.
        (default: checks on)

       -C <double>
        The complexity constant C. (default 1)

       -N
        Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)

       -L <double>
        The tolerance parameter. (default 1.0e-3)

       -P <double>
        The epsilon for round-off error. (default 1.0e-12)

       -M
        Fit logistic models to SVM outputs.

       -V <double>
        The number of folds for the internal
        cross-validation. (default -1, use training data)

       -W <double>
        The random number seed. (default 1)

       -K <classname and parameters>
        The Kernel to use.
        (default: weka.classifiers.functions.supportVector.PolyKernel)

       Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:

       -D
        Enables debugging output (if available) to be printed.
        (default: off)

       -no-checks
        Turns off all checks - use with caution!
        (default: checks on)

       -C <num>
        The size of the cache (a prime number), 0 for full cache and
        -1 to turn it off.
        (default: 250007)

       -E <num>
        The Exponent to use.
        (default: 1.0)

       -L
        Use lower-order terms.
        (default: no)

  Options after -- are passed to the designated classifier.

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

### getNumFolds

    public int getNumFolds()

  Gets the number of folds to use for splitting the training set.

  Returns:
    the number of folds

### setNumFolds

    public void setNumFolds(int value)

  Sets the number of folds to use for splitting the training set.

  Parameters:
    `value` - the new number of folds

### numFoldsTipText

    public java.lang.String numFoldsTipText()

  Returns the tip text for this property

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

### setVerbose

    public void setVerbose(boolean value)

  Set the verbose state.

  Parameters:
    `value` - the verbose state

### getVerbose

    public boolean getVerbose()

  Gets the verbose state

  Returns:
    the verbose state

### verboseTipText

    public java.lang.String verboseTipText()

  Returns the tip text for this property

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

### buildClassifier

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

  Bagging method.

  Specified by:
    `buildClassifier` in interface `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

### distributionForInstance

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

  Calculates the class membership probabilities for the given test instance.

  Specified by:
    `distributionForInstance` in interface `Classifier`
  Overrides:
    `distributionForInstance` in class `AbstractClassifier`
  Parameters:
    `instance` - the instance to be classified
  Returns:
    preedicted class probability distribution
  Throws:
    `java.lang.Exception` - if distribution can't be computed successfully

### toString

    public java.lang.String toString()

  Returns description of the classifier.

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

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

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

### main

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

  Main method for testing this class.

  Parameters:
    `args` - the options

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