SimpleCart (weka-stable 3.6.8 API)

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

All Implemented Interfaces:: Serializable, 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: 5535 $
Author:: Haijian Shi (hs69@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected double`	`m_Alpha` Alpha-value (for pruning) at the node.
`protected Attribute`	`m_Attribute` Attribute used to split data.
`protected Attribute`	`m_ClassAttribute` Class attriubte of data.
`protected double[]`	`m_ClassProbs` Class probabilities.
`protected double`	`m_ClassValue` Class value if the node is leaf.
`protected double[]`	`m_Distribution` Distributions of leaf node (or temporary leaf node in minimal cost-complexity pruning)
`protected boolean`	`m_Heuristic` If use huristic search for nominal attributes in multi-class problems (default true).
`protected boolean`	`m_isLeaf` Indicate if the node is a leaf node.
`protected double`	`m_minNumObj` Minimum number of instances in at the terminal nodes.
`protected int`	`m_numFoldsPruning` Number of folds for minimal cost-complexity pruning.
`protected double`	`m_numIncorrectModel` Number of training examples misclassified by the model (subtree rooted).
`protected double`	`m_numIncorrectTree` Number of training examples misclassified by the model (subtree not rooted).
`protected double[]`	`m_Props` Proportion for each branch.
`protected boolean`	`m_Prune` If use minimal cost-compexity pruning.
`protected double`	`m_SizePer` Training data size.
`protected String`	`m_SplitString` Split subset used to split data for nominal attributes.
`protected double`	`m_SplitValue` Split point for a numeric attribute.
`protected SimpleCart[]`	`m_Successors` Successor nodes.
`protected int`	`m_totalTrainInstances` Total number of instances used to build the classifier.
`protected Instances`	`m_train` Training data.
`protected boolean`	`m_UseOneSE` If use the 1SE rule to make final decision tree.

Fields inherited from class weka.classifiers.RandomizableClassifier
m_Seed

Fields inherited from class weka.classifiers.Classifier
m_Debug

Constructor Summary

Constructors
Constructor and Description

SimpleCart()

Constructors
Constructor and Description
`SimpleCart()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Build the classifier.
`void`	`calculateAlphas()` Updates the alpha field for all nodes.
`protected double`	`computeGini(double[] dist, double total)` Compute and return gini index for a given distribution of a node.
`protected double`	`computeGiniGain(double[] parentDist, double[][] childDist)` Compute and return gini gain for given distributions of a node and its successor nodes.
`protected double`	`computeSortedInfo(Instances data, int[][] sortedIndices, double[][] weights, double[] classProbs)` Compute sorted indices, weights and class probabilities for a given dataset.
`double[]`	`distributionForInstance(Instance instance)` Computes class probabilities for instance using the decision tree.
`Enumeration`	`enumerateMeasures()` Return an enumeration of the measure names.
`protected void`	`fillInnerNodes(Vector nodeList)` Fills a list with all inner nodes in the tree.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getHeuristic()` Get if use heuristic search for nominal attributes in multi-class problems.
`protected Vector`	`getInnerNodes()` Return a list of all inner nodes in the tree.
`double`	`getMeasure(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.
`String[]`	`getOptions()` Gets the current settings of the classifier.
`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.
`String`	`globalInfo()` Return a description suitable for displaying in the explorer/experimenter.
`String`	`heuristicTipText()` Returns the tip text for this property
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] args)` Main method.
`protected void`	`makeLeaf(Instances data)` Make the node leaf node.
`protected void`	`makeTree(Instances data, int totalInstances, int[][] sortedIndices, double[][] weights, double[] classProbs, double totalWeight, double minNumObj, boolean useHeuristic)` Make binary decision tree recursively.
`double`	`measureTreeSize()` Return number of tree size.
`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.
`protected SimpleCart`	`nodeToPrune(Vector nodeList)` Find the node with minimal alpha value.
`protected String`	`nominalDistribution(double[][] props, double[][][] dists, Attribute att, int[] sortedIndices, double[] weights, double[][] subsetWeights, double[] giniGains, Instances data, boolean useHeuristic)` Compute distributions, proportions and total weights of two successor nodes for a given nominal attribute.
`protected double`	`numericDistribution(double[][] props, double[][][] dists, Attribute att, int[] sortedIndices, double[] weights, double[][] subsetWeights, double[] giniGains, Instances data)` Compute distributions, proportions and total weights of two successor nodes for a given numeric attribute.
`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(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.
`String`	`sizePerTipText()` Returns the tip text for this property
`protected void`	`splitData(int[][][] subsetIndices, double[][][] subsetWeights, Attribute att, double splitPoint, String splitStr, int[][] sortedIndices, double[][] weights, Instances data)` Split data into two subsets and store sorted indices and weights for two successor nodes.
`String`	`toString()` Prints the decision tree using the protected toString method from below.
`protected String`	`toString(int level)` Outputs a tree at a certain level.
`void`	`treeErrors()` Updates the numIncorrectTree field for all nodes.
`protected void`	`unprune()` Method to "unprune" the CART tree.
`String`	`useOneSETipText()` Returns the tip text for this property
`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, runClassifier, setDebug

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

- Field Detail
  - m_train
```
protected Instances m_train
```
    Training data.
  - m_Successors
```
protected SimpleCart[] m_Successors
```
    Successor nodes.
  - m_Attribute
```
protected Attribute m_Attribute
```
    Attribute used to split data.
  - m_SplitValue
```
protected double m_SplitValue
```
    Split point for a numeric attribute.
  - m_SplitString
```
protected String m_SplitString
```
    Split subset used to split data for nominal attributes.
  - m_ClassValue
```
protected double m_ClassValue
```
    Class value if the node is leaf.
  - m_ClassAttribute
```
protected Attribute m_ClassAttribute
```
    Class attriubte of data.
  - m_minNumObj
```
protected double m_minNumObj
```
    Minimum number of instances in at the terminal nodes.
  - m_numFoldsPruning
```
protected int m_numFoldsPruning
```
    Number of folds for minimal cost-complexity pruning.
  - m_Alpha
```
protected double m_Alpha
```
    Alpha-value (for pruning) at the node.
  - m_numIncorrectModel
```
protected double m_numIncorrectModel
```
    Number of training examples misclassified by the model (subtree rooted).
  - m_numIncorrectTree
```
protected double m_numIncorrectTree
```
    Number of training examples misclassified by the model (subtree not rooted).
  - m_isLeaf
```
protected boolean m_isLeaf
```
    Indicate if the node is a leaf node.
  - m_Prune
```
protected boolean m_Prune
```
    If use minimal cost-compexity pruning.
  - m_totalTrainInstances
```
protected int m_totalTrainInstances
```
    Total number of instances used to build the classifier.
  - m_Props
```
protected double[] m_Props
```
    Proportion for each branch.
  - m_ClassProbs
```
protected double[] m_ClassProbs
```
    Class probabilities.
  - m_Distribution
```
protected double[] m_Distribution
```
    Distributions of leaf node (or temporary leaf node in minimal cost-complexity pruning)
  - m_Heuristic
```
protected boolean m_Heuristic
```
    If use huristic search for nominal attributes in multi-class problems (default true).
  - m_UseOneSE
```
protected boolean m_UseOneSE
```
    If use the 1SE rule to make final decision tree.
  - m_SizePer
```
protected double m_SizePer
```
    Training data size.
- Constructor Detail
  - SimpleCart
```
public SimpleCart()
```
- Method Detail
  - globalInfo
```
public 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 Exception
```
    Build the classifier.
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    data - the training instances
    
    Throws:
    
    Exception - if something goes wrong
  - makeTree
```
protected void makeTree(Instances data,
                        int totalInstances,
                        int[][] sortedIndices,
                        double[][] weights,
                        double[] classProbs,
                        double totalWeight,
                        double minNumObj,
                        boolean useHeuristic)
                 throws Exception
```
    Make binary decision tree recursively.
    
    Parameters:
    
    data - the training instances
    
    totalInstances - total number of instances
    
    sortedIndices - sorted indices of the instances
    
    weights - weights of the instances
    
    classProbs - class probabilities
    
    totalWeight - total weight of instances
    
    minNumObj - minimal number of instances at leaf nodes
    
    useHeuristic - if use heuristic search for nominal attributes in multi-class problem
    
    Throws:
    
    Exception - if something goes wrong
  - prune
```
public void prune(double alpha)
           throws Exception
```
    Prunes the original tree using the CART pruning scheme, given a cost-complexity parameter alpha.
    
    Parameters:
    
    alpha - the cost-complexity parameter
    
    Throws:
    
    Exception - if something goes wrong
  - prune
```
public int prune(double[] alphas,
                 double[] errors,
                 Instances test)
          throws 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:
    
    Exception - if something goes wrong
  - unprune
```
protected void unprune()
```
    Method to "unprune" the CART tree. Sets all leaf-fields to false. Faster than re-growing the tree because CART do not have to be fit again.
  - numericDistribution
```
protected double numericDistribution(double[][] props,
                                     double[][][] dists,
                                     Attribute att,
                                     int[] sortedIndices,
                                     double[] weights,
                                     double[][] subsetWeights,
                                     double[] giniGains,
                                     Instances data)
                              throws Exception
```
    Compute distributions, proportions and total weights of two successor nodes for a given numeric attribute.
    
    Parameters:
    
    props - proportions of each two branches for each attribute
    
    dists - class distributions of two branches for each attribute
    
    att - numeric att split on
    
    sortedIndices - sorted indices of instances for the attirubte
    
    weights - weights of instances for the attirbute
    
    subsetWeights - total weight of two branches split based on the attribute
    
    giniGains - Gini gains for each attribute
    
    data - training instances
    
    Returns:
    
    Gini gain the given numeric attribute
    
    Throws:
    
    Exception - if something goes wrong
  - nominalDistribution
```
protected String nominalDistribution(double[][] props,
                                     double[][][] dists,
                                     Attribute att,
                                     int[] sortedIndices,
                                     double[] weights,
                                     double[][] subsetWeights,
                                     double[] giniGains,
                                     Instances data,
                                     boolean useHeuristic)
                              throws Exception
```
    Compute distributions, proportions and total weights of two successor nodes for a given nominal attribute.
    
    Parameters:
    
    props - proportions of each two branches for each attribute
    
    dists - class distributions of two branches for each attribute
    
    att - numeric att split on
    
    sortedIndices - sorted indices of instances for the attirubte
    
    weights - weights of instances for the attirbute
    
    subsetWeights - total weight of two branches split based on the attribute
    
    giniGains - Gini gains for each attribute
    
    data - training instances
    
    useHeuristic - if use heuristic search
    
    Returns:
    
    Gini gain for the given nominal attribute
    
    Throws:
    
    Exception - if something goes wrong
  - splitData
```
protected void splitData(int[][][] subsetIndices,
                         double[][][] subsetWeights,
                         Attribute att,
                         double splitPoint,
                         String splitStr,
                         int[][] sortedIndices,
                         double[][] weights,
                         Instances data)
                  throws Exception
```
    Split data into two subsets and store sorted indices and weights for two successor nodes.
    
    Parameters:
    
    subsetIndices - sorted indecis of instances for each attribute for two successor node
    
    subsetWeights - weights of instances for each attribute for two successor node
    
    att - attribute the split based on
    
    splitPoint - split point the split based on if att is numeric
    
    splitStr - split subset the split based on if att is nominal
    
    sortedIndices - sorted indices of the instances to be split
    
    weights - weights of the instances to bes split
    
    data - training data
    
    Throws:
    
    Exception - if something goes wrong
  - modelErrors
```
public void modelErrors()
                 throws Exception
```
    Updates the numIncorrectModel field for all nodes when subtree (to be pruned) is rooted. This is needed for calculating the alpha-values.
    
    Throws:
    
    Exception - if something goes wrong
  - treeErrors
```
public void treeErrors()
                throws Exception
```
    Updates the numIncorrectTree field for all nodes. This is needed for calculating the alpha-values.
    
    Throws:
    
    Exception - if something goes wrong
  - calculateAlphas
```
public void calculateAlphas()
                     throws Exception
```
    Updates the alpha field for all nodes.
    
    Throws:
    
    Exception - if something goes wrong
  - nodeToPrune
```
protected SimpleCart nodeToPrune(Vector nodeList)
```
    Find the node with minimal alpha value. If two nodes have the same alpha, choose the one with more leave nodes.
    
    Parameters:
    
    nodeList - list of inner nodes
    
    Returns:
    
    the node to be pruned
  - computeSortedInfo
```
protected double computeSortedInfo(Instances data,
                                   int[][] sortedIndices,
                                   double[][] weights,
                                   double[] classProbs)
                            throws Exception
```
    Compute sorted indices, weights and class probabilities for a given dataset. Return total weights of the data at the node.
    
    Parameters:
    
    data - training data
    
    sortedIndices - sorted indices of instances at the node
    
    weights - weights of instances at the node
    
    classProbs - class probabilities at the node
    
    Returns:
    
    total weights of instances at the node
    
    Throws:
    
    Exception - if something goes wrong
  - computeGiniGain
```
protected double computeGiniGain(double[] parentDist,
                                 double[][] childDist)
```
    Compute and return gini gain for given distributions of a node and its successor nodes.
    
    Parameters:
    
    parentDist - class distributions of parent node
    
    childDist - class distributions of successor nodes
    
    Returns:
    
    Gini gain computed
  - computeGini
```
protected double computeGini(double[] dist,
                             double total)
```
    Compute and return gini index for a given distribution of a node.
    
    Parameters:
    
    dist - class distributions
    
    total - class distributions
    
    Returns:
    
    Gini index of the class distributions
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws 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:
    
    Exception - if something goes wrong
  - makeLeaf
```
protected void makeLeaf(Instances data)
```
    Make the node leaf node.
    
    Parameters:
    
    data - trainging data
  - toString
```
public String toString()
```
    Prints the decision tree using the protected toString method from below.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a textual description of the classifier
  - toString
```
protected String toString(int level)
```
    Outputs a tree at a certain level.
    
    Parameters:
    
    level - the level at which the tree is to be printed
    
    Returns:
    
    a tree at a certain level
  - 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
  - getInnerNodes
```
protected Vector getInnerNodes()
```
    Return a list of all inner nodes in the tree.
    
    Returns:
    
    the list of all inner nodes
  - fillInnerNodes
```
protected void fillInnerNodes(Vector nodeList)
```
    Fills a list with all inner nodes in the tree.
    
    Parameters:
    
    nodeList - the list to be filled
  - numLeaves
```
public int numLeaves()
```
    Compute number of leaf nodes.
    
    Returns:
    
    number of leaf nodes
  - listOptions
```
public 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(String[] options)
                throws 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:
    
    Exception - if an options is not supported
  - getOptions
```
public 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 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(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:
    
    IllegalArgumentException - if the named measure is not supported
  - minNumObjTipText
```
public 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 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 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 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 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 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 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(String[] args)
```
    Main method.
    
    Parameters:
    
    args - the options for the classifier

Class SimpleCart

Field Summary

Fields inherited from class weka.classifiers.RandomizableClassifier

Fields inherited from class weka.classifiers.Classifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

m_train

m_Successors

m_Attribute

m_SplitValue

m_SplitString

m_ClassValue

m_ClassAttribute

m_minNumObj

m_numFoldsPruning

m_Alpha

m_numIncorrectModel

m_numIncorrectTree

m_isLeaf

m_Prune

m_totalTrainInstances

m_Props

m_ClassProbs

m_Distribution

m_Heuristic

m_UseOneSE

m_SizePer

Constructor Detail

SimpleCart

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

makeTree

prune

prune

unprune

numericDistribution

nominalDistribution

splitData

modelErrors

treeErrors

calculateAlphas

nodeToPrune

computeSortedInfo

computeGiniGain

computeGini

distributionForInstance

makeLeaf

toString

toString

numNodes

numInnerNodes

getInnerNodes

fillInnerNodes

numLeaves

listOptions

setOptions

getOptions

enumerateMeasures

measureTreeSize

getMeasure

minNumObjTipText

setMinNumObj

getMinNumObj

numFoldsPruningTipText

setNumFoldsPruning

getNumFoldsPruning

usePruneTipText

setUsePrune

getUsePrune

heuristicTipText

setHeuristic

getHeuristic

useOneSETipText