SimpleCart

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## weka.classifiers.trees
Class SimpleCart

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.RandomizableClassifier
              weka.classifiers.trees.SimpleCart

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

    public class SimpleCart

extends RandomizableClassifier
implements AdditionalMeasureProducer, TechnicalInformationHandler

Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method.
For more information, see:
Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.

BibTeX:

     @book{Breiman1984,
        address = {Belmont, California},
        author = {Leo Breiman and Jerome H. Friedman and Richard A. Olshen and Charles J. Stone},
        publisher = {Wadsworth International Group},
        title = {Classification and Regression Trees},
        year = {1984}
     }

Valid options are:

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

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

     -M <min no>
      The minimal number of instances at the terminal nodes.
      (default 2)

     -N <num folds>
      The number of folds used in the minimal cost-complexity pruning.
      (default 5)

     -U
      Don't use the minimal cost-complexity pruning.
      (default yes).

     -H
      Don't use the heuristic method for binary split.
      (default true).

     -A
      Use 1 SE rule to make pruning decision.
      (default no).

     -C
      Percentage of training data size (0-1].
      (default 1).

Version:
  $Revision: 1.4 $
Author:
  Haijian Shi (hs69@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`SimpleCart()`

Method Summary
`void` | `buildClassifier(Instances data)` Build the classifier.
`void` | `calculateAlphas()` Updates the alpha field for all nodes.
`double[]` | `distributionForInstance(Instance instance)` Computes class probabilities for instance using the decision tree.
`java.util.Enumeration` | `enumerateMeasures()` Return an enumeration of the measure names.
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`boolean` | `getHeuristic()` Get if use heuristic search for nominal attributes in multi-class problems.
`double` | `getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure.
`double` | `getMinNumObj()` Get minimal number of instances at the terminal nodes.
`int` | `getNumFoldsPruning()` Set number of folds in internal cross-validation.
`java.lang.String[]` | `getOptions()` Gets the current settings of the classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`double` | `getSizePer()` Get training set size.
`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` | `getUseOneSE()` Get if use the 1SE rule to choose final model.
`boolean` | `getUsePrune()` Get if use minimal cost-complexity pruning.
`java.lang.String` | `globalInfo()` Return a description suitable for displaying in the explorer/experimenter.
`java.lang.String` | `heuristicTipText()` Returns the tip text for this property
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] args)` Main method.
`double` | `measureTreeSize()` Return number of tree size.
`java.lang.String` | `minNumObjTipText()` Returns the tip text for this property
`void` | `modelErrors()` Updates the numIncorrectModel field for all nodes when subtree (to be pruned) is rooted.
`java.lang.String` | `numFoldsPruningTipText()` Returns the tip text for this property
`int` | `numInnerNodes()` Method to count the number of inner nodes in the tree.
`int` | `numLeaves()` Compute number of leaf nodes.
`int` | `numNodes()` Compute size of the tree.
`void` | `prune(double alpha)` Prunes the original tree using the CART pruning scheme, given a cost-complexity parameter alpha.
`int` | `prune(double[] alphas, double[] errors, Instances test)` Method for performing one fold in the cross-validation of minimal cost-complexity pruning.
`void` | `setHeuristic(boolean value)` Set if use heuristic search for nominal attributes in multi-class problems.
`void` | `setMinNumObj(double value)` Set minimal number of instances at the terminal nodes.
`void` | `setNumFoldsPruning(int value)` Set number of folds in internal cross-validation.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setSizePer(double value)` Set training set size.
`void` | `setUseOneSE(boolean value)` Set if use the 1SE rule to choose final model.
`void` | `setUsePrune(boolean value)` Set if use minimal cost-complexity pruning.
`java.lang.String` | `sizePerTipText()` Returns the tip text for this property
`java.lang.String` | `toString()` Prints the decision tree using the protected toString method from below.
`void` | `treeErrors()` Updates the numIncorrectTree field for all nodes.
`java.lang.String` | `useOneSETipText()` Returns the tip text for this property
`java.lang.String` | `usePruneTipText()` Return the tip text for this property

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

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

### SimpleCart

    public SimpleCart()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Return a description suitable for displaying in the explorer/experimenter.

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

### 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

### 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

  Build the classifier.

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `data` - the training instances
  Throws:
    `java.lang.Exception` - if something goes wrong

### prune

    public void prune(double alpha)
               throws java.lang.Exception

  Prunes the original tree using the CART pruning scheme, given a cost-complexity parameter alpha.

  Parameters:
    `alpha` - the cost-complexity parameter
  Throws:
    `java.lang.Exception` - if something goes wrong

### prune

    public int prune(double[] alphas,
                     double[] errors,
                     Instances test)
              throws java.lang.Exception

  Method for performing one fold in the cross-validation of minimal cost-complexity pruning. Generates a sequence of alpha-values with error estimates for the corresponding (partially pruned) trees, given the test set of that fold.

  Parameters:
    `alphas` - array to hold the generated alpha-values
    `errors` - array to hold the corresponding error estimates
    `test` - test set of that fold (to obtain error estimates)
  Returns:
    the iteration of the pruning
  Throws:
    `java.lang.Exception` - if something goes wrong

### modelErrors

    public void modelErrors()
                     throws java.lang.Exception

  Updates the numIncorrectModel field for all nodes when subtree (to be pruned) is rooted. This is needed for calculating the alpha-values.

  Throws:
    `java.lang.Exception` - if something goes wrong

### treeErrors

    public void treeErrors()
                    throws java.lang.Exception

  Updates the numIncorrectTree field for all nodes. This is needed for calculating the alpha-values.

  Throws:
    `java.lang.Exception` - if something goes wrong

### calculateAlphas

    public void calculateAlphas()
                         throws java.lang.Exception

  Updates the alpha field for all nodes.

  Throws:
    `java.lang.Exception` - if something goes wrong

### distributionForInstance

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

  Computes class probabilities for instance using the decision tree.

  Overrides:
    `distributionForInstance` in class `Classifier`
  Parameters:
    `instance` - the instance for which class probabilities is to be computed
  Returns:
    the class probabilities for the given instance
  Throws:
    `java.lang.Exception` - if something goes wrong

### toString

    public java.lang.String toString()

  Prints the decision tree using the protected toString method from below.

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

### numNodes

    public int numNodes()

  Compute size of the tree.

  Returns:
    size of the tree

### numInnerNodes

    public int numInnerNodes()

  Method to count the number of inner nodes in the tree.

  Returns:
    the number of inner nodes

### numLeaves

    public int numLeaves()

  Compute number of leaf nodes.

  Returns:
    number of leaf nodes

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

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

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

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

       -M <min no>
        The minimal number of instances at the terminal nodes.
        (default 2)

       -N <num folds>
        The number of folds used in the minimal cost-complexity pruning.
        (default 5)

       -U
        Don't use the minimal cost-complexity pruning.
        (default yes).

       -H
        Don't use the heuristic method for binary split.
        (default true).

       -A
        Use 1 SE rule to make pruning decision.
        (default no).

       -C
        Percentage of training data size (0-1].
        (default 1).

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `RandomizableClassifier`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an options 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 `RandomizableClassifier`
  Returns:
    the current setting of the classifier

### enumerateMeasures

    public java.util.Enumeration enumerateMeasures()

  Return an enumeration of the measure names.

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

### measureTreeSize

    public double measureTreeSize()

  Return number of tree size.

  Returns:
    number of tree size

### 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

### minNumObjTipText

    public java.lang.String minNumObjTipText()

  Returns the tip text for this property

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

### setMinNumObj

    public void setMinNumObj(double value)

  Set minimal number of instances at the terminal nodes.

  Parameters:
    `value` - minimal number of instances at the terminal nodes

### getMinNumObj

    public double getMinNumObj()

  Get minimal number of instances at the terminal nodes.

  Returns:
    minimal number of instances at the terminal nodes

### numFoldsPruningTipText

    public java.lang.String numFoldsPruningTipText()

  Returns the tip text for this property

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

### setNumFoldsPruning

    public void setNumFoldsPruning(int value)

  Set number of folds in internal cross-validation.

  Parameters:
    `value` - number of folds in internal cross-validation.

### getNumFoldsPruning

    public int getNumFoldsPruning()

  Set number of folds in internal cross-validation.

  Returns:
    number of folds in internal cross-validation.

### usePruneTipText

    public java.lang.String usePruneTipText()

  Return the tip text for this property

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

### setUsePrune

    public void setUsePrune(boolean value)

  Set if use minimal cost-complexity pruning.

  Parameters:
    `value` - if use minimal cost-complexity pruning

### getUsePrune

    public boolean getUsePrune()

  Get if use minimal cost-complexity pruning.

  Returns:
    if use minimal cost-complexity pruning

### heuristicTipText

    public java.lang.String heuristicTipText()

  Returns the tip text for this property

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

### setHeuristic

    public void setHeuristic(boolean value)

  Set if use heuristic search for nominal attributes in multi-class problems.

  Parameters:
    `value` - if use heuristic search for nominal attributes in multi-class problems

### getHeuristic

    public boolean getHeuristic()

  Get if use heuristic search for nominal attributes in multi-class problems.

  Returns:
    if use heuristic search for nominal attributes in multi-class problems

### useOneSETipText

    public java.lang.String useOneSETipText()

  Returns the tip text for this property

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

### setUseOneSE

    public void setUseOneSE(boolean value)

  Set if use the 1SE rule to choose final model.

  Parameters:
    `value` - if use the 1SE rule to choose final model

### getUseOneSE

    public boolean getUseOneSE()

  Get if use the 1SE rule to choose final model.

  Returns:
    if use the 1SE rule to choose final model

### sizePerTipText

    public java.lang.String sizePerTipText()

  Returns the tip text for this property

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

### setSizePer

    public void setSizePer(double value)

  Set training set size.

  Parameters:
    `value` - training set size

### getSizePer

    public double getSizePer()

  Get training set size.

  Returns:
    training set size

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Returns:
    the revision

### main

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

  Main method.

  Parameters:
    `args` - the options for the classifier

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