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## weka.classifiers.meta
Class LogitBoost

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

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

    public class LogitBoost

extends RandomizableIteratedSingleClassifierEnhancer
implements Sourcable, WeightedInstancesHandler

Class for performing additive logistic regression.. This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see

Friedman, J., T. Hastie and R. Tibshirani (1998) Additive Logistic Regression: a Statistical View of Boosting download postscript.

Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a weak learner as the basis for boosting (required).

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

-Q
Use resampling instead of reweighting.

-S seed
Random number seed for resampling (default 1).

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-F num
Set number of folds for the internal cross-validation (default 0 -- no cross-validation).

-R num
Set number of runs for the internal cross-validation (default 1).

-L num
Set the threshold for the improvement of the average loglikelihood (default -Double.MAX_VALUE).

-H num
Set the value of the shrinkage parameter (default 1).

Options after -- are passed to the designated learner.

Version:
  $Revision: 1.33 $
Author:
  Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`LogitBoost()` Constructor.

Method Summary
`void` | `buildClassifier(Instances data)` Builds the boosted classifier
`Classifier[][]` | `classifiers()` Returns the array of classifiers that have been built.
`double[]` | `distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`double` | `getLikelihoodThreshold()` Get the value of Precision.
`int` | `getNumFolds()` Get the value of NumFolds.
`int` | `getNumRuns()` Get the value of NumRuns.
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`double` | `getShrinkage()` Get the value of Shrinkage.
`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.lang.String` | `likelihoodThresholdTipText()` Returns the tip text for this property
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String` | `numFoldsTipText()` Returns the tip text for this property
`java.lang.String` | `numRunsTipText()` Returns the tip text for this property
`void` | `setLikelihoodThreshold(double newPrecision)` Set the value of Precision.
`void` | `setNumFolds(int newNumFolds)` Set the value of NumFolds.
`void` | `setNumRuns(int newNumRuns)` Set the value of NumRuns.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setShrinkage(double newShrinkage)` Set the value of Shrinkage.
`void` | `setUseResampling(boolean r)` Set resampling mode
`void` | `setWeightThreshold(int threshold)` Set weight thresholding
`java.lang.String` | `shrinkageTipText()` Returns the tip text for this property
`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

### LogitBoost

    public LogitBoost()

  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

### 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:

  -D
  Turn on debugging output.

  -W classname
  Specify the full class name of a weak learner as the basis for boosting (required).

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

  -Q
  Use resampling instead of reweighting.

  -S seed
  Random number seed for resampling (default 1).

  -P num
  Set the percentage of weight mass used to build classifiers (default 100).

  -F num
  Set number of folds for the internal cross-validation (default 0 -- no cross-validation).

  -R num
  Set number of runs for the internal cross-validation (default 1.

  -L num
  Set the threshold for the improvement of the average loglikelihood (default -Double.MAX_VALUE).

  -H num
  Set the value of the shrinkage parameter (default 1).

  Options after -- are passed to the designated learner.

  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

### shrinkageTipText

    public java.lang.String shrinkageTipText()

  Returns the tip text for this property

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

### getShrinkage

    public double getShrinkage()

  Get the value of Shrinkage.

  Returns:
    Value of Shrinkage.

### setShrinkage

    public void setShrinkage(double newShrinkage)

  Set the value of Shrinkage.

  Parameters:
    `newShrinkage` - Value to assign to Shrinkage.

### likelihoodThresholdTipText

    public java.lang.String likelihoodThresholdTipText()

  Returns the tip text for this property

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

### getLikelihoodThreshold

    public double getLikelihoodThreshold()

  Get the value of Precision.

  Returns:
    Value of Precision.

### setLikelihoodThreshold

    public void setLikelihoodThreshold(double newPrecision)

  Set the value of Precision.

  Parameters:
    `newPrecision` - Value to assign to Precision.

### numRunsTipText

    public java.lang.String numRunsTipText()

  Returns the tip text for this property

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

### getNumRuns

    public int getNumRuns()

  Get the value of NumRuns.

  Returns:
    Value of NumRuns.

### setNumRuns

    public void setNumRuns(int newNumRuns)

  Set the value of NumRuns.

  Parameters:
    `newNumRuns` - Value to assign to NumRuns.

### 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

### getNumFolds

    public int getNumFolds()

  Get the value of NumFolds.

  Returns:
    Value of NumFolds.

### setNumFolds

    public void setNumFolds(int newNumFolds)

  Set the value of NumFolds.

  Parameters:
    `newNumFolds` - Value to assign to NumFolds.

### 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:
    `resampling` - true if resampling should be done

### getUseResampling

    public boolean getUseResampling()

  Get whether resampling is turned on

  Returns:
    true if resampling output is on

### 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 thresholding

  Parameters:
    `thresholding` - 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

### buildClassifier

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

  Builds the boosted classifier

  Overrides:
    `buildClassifier` in class `IteratedSingleClassifierEnhancer`
  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

### classifiers

    public Classifier[][] classifiers()

  Returns the array of classifiers that have been built.

### 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 name that should be given to the source 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

### main

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

  Main method for testing this class.

  Parameters:
    `argv` - the options

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