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## weka.classifiers.trees
Class REPTree

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.trees.REPTree

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

    public class REPTree

extends Classifier
implements OptionHandler, WeightedInstancesHandler, Drawable, AdditionalMeasureProducer, Sourcable

Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting). Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5).

Valid options are:

     -M <minimum number of instances>
      Set minimum number of instances per leaf (default 2).

     -V <minimum variance for split>
      Set minimum numeric class variance proportion
      of train variance for split (default 1e-3).

     -N <number of folds>
      Number of folds for reduced error pruning (default 3).

     -S <seed>
      Seed for random data shuffling (default 1).

     -P
      No pruning.

     -L
      Maximum tree depth (default -1, no maximum)

Version:
  $Revision: 6952 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary

Fields inherited from interface weka.core.Drawable
`BayesNet, Newick, NOT_DRAWABLE, TREE`

Constructor Summary
`REPTree()`

Method Summary
`void` | `buildClassifier(Instances data)` Builds classifier.
`double[]` | `distributionForInstance(Instance instance)` Computes class distribution of an instance using the tree.
`java.util.Enumeration` | `enumerateMeasures()` Returns an enumeration of the additional measure names.
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`int` | `getMaxDepth()` Get the value of MaxDepth.
`double` | `getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure.
`double` | `getMinNum()` Get the value of MinNum.
`double` | `getMinVarianceProp()` Get the value of MinVarianceProp.
`boolean` | `getNoPruning()` Get the value of NoPruning.
`int` | `getNumFolds()` Get the value of NumFolds.
`java.lang.String[]` | `getOptions()` Gets options from this classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`int` | `getSeed()` Get the value of Seed.
`java.lang.String` | `globalInfo()` Returns a string describing classifier
`java.lang.String` | `graph()` Outputs the decision tree as a graph
`int` | `graphType()` Returns the type of graph this classifier represents.
`java.util.Enumeration` | `listOptions()` Lists the command-line options for this classifier.
`static void` | `main(java.lang.String[] argv)` Main method for this class.
`java.lang.String` | `maxDepthTipText()` Returns the tip text for this property
`java.lang.String` | `minNumTipText()` Returns the tip text for this property
`java.lang.String` | `minVariancePropTipText()` Returns the tip text for this property
`java.lang.String` | `noPruningTipText()` Returns the tip text for this property
`java.lang.String` | `numFoldsTipText()` Returns the tip text for this property
`int` | `numNodes()` Computes size of the tree.
`java.lang.String` | `seedTipText()` Returns the tip text for this property
`void` | `setMaxDepth(int newMaxDepth)` Set the value of MaxDepth.
`void` | `setMinNum(double newMinNum)` Set the value of MinNum.
`void` | `setMinVarianceProp(double newMinVarianceProp)` Set the value of MinVarianceProp.
`void` | `setNoPruning(boolean newNoPruning)` Set the value of NoPruning.
`void` | `setNumFolds(int newNumFolds)` Set the value of NumFolds.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setSeed(int newSeed)` Set the value of Seed.
`java.lang.String` | `toSource(java.lang.String className)` Returns the tree as if-then statements.
`java.lang.String` | `toString()` Outputs the decision tree.

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

### REPTree

    public REPTree()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing classifier

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

### noPruningTipText

    public java.lang.String noPruningTipText()

  Returns the tip text for this property

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

### getNoPruning

    public boolean getNoPruning()

  Get the value of NoPruning.

  Returns:
    Value of NoPruning.

### setNoPruning

    public void setNoPruning(boolean newNoPruning)

  Set the value of NoPruning.

  Parameters:
    `newNoPruning` - Value to assign to NoPruning.

### minNumTipText

    public java.lang.String minNumTipText()

  Returns the tip text for this property

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

### getMinNum

    public double getMinNum()

  Get the value of MinNum.

  Returns:
    Value of MinNum.

### setMinNum

    public void setMinNum(double newMinNum)

  Set the value of MinNum.

  Parameters:
    `newMinNum` - Value to assign to MinNum.

### minVariancePropTipText

    public java.lang.String minVariancePropTipText()

  Returns the tip text for this property

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

### getMinVarianceProp

    public double getMinVarianceProp()

  Get the value of MinVarianceProp.

  Returns:
    Value of MinVarianceProp.

### setMinVarianceProp

    public void setMinVarianceProp(double newMinVarianceProp)

  Set the value of MinVarianceProp.

  Parameters:
    `newMinVarianceProp` - Value to assign to MinVarianceProp.

### seedTipText

    public java.lang.String seedTipText()

  Returns the tip text for this property

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

### getSeed

    public int getSeed()

  Get the value of Seed.

  Returns:
    Value of Seed.

### setSeed

    public void setSeed(int newSeed)

  Set the value of Seed.

  Parameters:
    `newSeed` - Value to assign to Seed.

### 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.

### maxDepthTipText

    public java.lang.String maxDepthTipText()

  Returns the tip text for this property

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

### getMaxDepth

    public int getMaxDepth()

  Get the value of MaxDepth.

  Returns:
    Value of MaxDepth.

### setMaxDepth

    public void setMaxDepth(int newMaxDepth)

  Set the value of MaxDepth.

  Parameters:
    `newMaxDepth` - Value to assign to MaxDepth.

### listOptions

    public java.util.Enumeration listOptions()

  Lists the command-line options for this classifier.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `Classifier`
  Returns:
    an enumeration over all commandline options

### getOptions

    public java.lang.String[] getOptions()

  Gets options from this classifier.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `Classifier`
  Returns:
    the options for the current setup

### setOptions

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

  Parses a given list of options.

  Valid options are:

       -M <minimum number of instances>
        Set minimum number of instances per leaf (default 2).

       -V <minimum variance for split>
        Set minimum numeric class variance proportion
        of train variance for split (default 1e-3).

       -N <number of folds>
        Number of folds for reduced error pruning (default 3).

       -S <seed>
        Seed for random data shuffling (default 1).

       -P
        No pruning.

       -L
        Maximum tree depth (default -1, no maximum)

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

### numNodes

    public int numNodes()

  Computes size of the tree.

  Returns:
    the number of nodes

### enumerateMeasures

    public java.util.Enumeration enumerateMeasures()

  Returns an enumeration of the additional measure names.

  Specified by:
    `enumerateMeasures` in interface `AdditionalMeasureProducer`
  Returns:
    an enumeration of the measure names

### getMeasure

    public double getMeasure(java.lang.String additionalMeasureName)

  Returns the value of the named measure.

  Specified by:
    `getMeasure` in interface `AdditionalMeasureProducer`
  Parameters:
    `additionalMeasureName` - the name of the measure to query for its value
  Returns:
    the value of the named measure
  Throws:
    `java.lang.IllegalArgumentException` - if the named measure is not supported

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

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

### buildClassifier

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

  Builds classifier.

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `data` - the data to train with
  Throws:
    `java.lang.Exception` - if building fails

### distributionForInstance

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

  Computes class distribution of an instance using the tree.

  Overrides:
    `distributionForInstance` in class `Classifier`
  Parameters:
    `instance` - the instance to compute the distribution for
  Returns:
    the computed class probabilities
  Throws:
    `java.lang.Exception` - if computation fails

### toSource

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

  Returns the tree as if-then statements.

  Specified by:
    `toSource` in interface `Sourcable`
  Parameters:
    `className` - the name for the generated class
  Returns:
    the tree as a Java if-then type statement
  Throws:
    `java.lang.Exception` - if something goes wrong

### graphType

    public int graphType()

  Returns the type of graph this classifier represents.

  Specified by:
    `graphType` in interface `Drawable`
  Returns:
    Drawable.TREE

### graph

    public java.lang.String graph()
                           throws java.lang.Exception

  Outputs the decision tree as a graph

  Specified by:
    `graph` in interface `Drawable`
  Returns:
    the tree as a graph
  Throws:
    `java.lang.Exception` - if generation fails

### toString

    public java.lang.String toString()

  Outputs the decision tree.

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

### 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 this class.

  Parameters:
    `argv` - the commandline options

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