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## weka.classifiers.trees.j48
Class C45PruneableClassifierTreeG

    java.lang.Object
      weka.classifiers.trees.j48.ClassifierTree
          weka.classifiers.trees.j48.C45PruneableClassifierTreeG

All Implemented Interfaces:
  java.io.Serializable, CapabilitiesHandler, Drawable, RevisionHandler

    public class C45PruneableClassifierTreeG

extends ClassifierTree

Class for handling a tree structure that can be pruned using C4.5 procedures and have nodes grafted on.

Version:
  $Revision: 5535 $
Author:
  Janice Boughton (based on code by Eibe Frank)
See Also:
  Serialized Form

Field Summary

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

Constructor Summary
`C45PruneableClassifierTreeG(ModelSelection toSelectLocModel, boolean pruneTree, float cf, boolean raiseTree, boolean relabel, boolean cleanup)` Constructor for pruneable tree structure.
`C45PruneableClassifierTreeG(ModelSelection toSelectLocModel, Instances data, ClassifierSplitModel gs, boolean prune, float cf, boolean raise, boolean isLeaf, boolean relabel, boolean cleanup)` Constructor for pruneable tree structure.

Method Summary
`double` | `biprob(double x, double n, double r)` Significance test
`void` | `buildClassifier(Instances data)` Method for building a pruneable classifier tree.
`void` | `collapse()` Collapses a tree to a node if training error doesn't increase.
`void` | `doGrafting(Instances data)` Initializes variables for grafting.
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier tree.
`java.lang.String` | `getRevision()` Returns the revision string.
`void` | `prune()` Prunes a tree using C4.5's pruning procedure.
`void` | `setDescendents(java.util.ArrayList t, C45PruneableClassifierTreeG originalLeaf)` add the grafted nodes at originalLeaf's position in tree.
`java.lang.String` | `toString()` Prints tree structure.

Methods inherited from class weka.classifiers.trees.j48.ClassifierTree
`assignIDs, buildTree, buildTree, classifyInstance, cleanup, distributionForInstance, graph, graphType, numLeaves, numNodes, prefix, toSource`

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

Constructor Detail

### C45PruneableClassifierTreeG

    public C45PruneableClassifierTreeG(ModelSelection toSelectLocModel,
                                       boolean pruneTree,
                                       float cf,
                                       boolean raiseTree,
                                       boolean relabel,
                                       boolean cleanup)
                                throws java.lang.Exception

  Constructor for pruneable tree structure. Stores reference to associated training data at each node.

  Parameters:
    `toSelectLocModel` - selection method for local splitting model
    `pruneTree` - true if the tree is to be pruned
    `cf` - the confidence factor for pruning
    `raiseTree` -
    `cleanup` -
  Throws:
    `java.lang.Exception` - if something goes wrong

### C45PruneableClassifierTreeG

    public C45PruneableClassifierTreeG(ModelSelection toSelectLocModel,
                                       Instances data,
                                       ClassifierSplitModel gs,
                                       boolean prune,
                                       float cf,
                                       boolean raise,
                                       boolean isLeaf,
                                       boolean relabel,
                                       boolean cleanup)

  Constructor for pruneable tree structure. Used to create new nodes in the tree during grafting.

  Parameters:
    `toSelectLocModel` - selection method for local splitting model
    `data` - the dta used to produce split model
    `gs` - the split model
    `prune` - true if the tree is to be pruned
    `cf` - the confidence factor for pruning
    `raise` -
    `isLeaf` - if this node is a leaf or not
    `relabel` - whether relabeling occured
    `cleanup` -
  Throws:
    `java.lang.Exception` - if something goes wrong

Method Detail

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier tree.

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

### buildClassifier

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

  Method for building a pruneable classifier tree.

  Overrides:
    `buildClassifier` in class `ClassifierTree`
  Parameters:
    `datathe` - data for building the tree
  Throws:
    `java.lang.Exception` - if something goes wrong

### collapse

    public final void collapse()

  Collapses a tree to a node if training error doesn't increase.

### prune

    public void prune()
               throws java.lang.Exception

  Prunes a tree using C4.5's pruning procedure.

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

### doGrafting

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

  Initializes variables for grafting. sets up limits array (for numeric attributes) and calls the recursive function traverseTree.

  Parameters:
    `data` - the data for the tree
  Throws:
    `java.lang.Exception` - if anything goes wrong

### setDescendents

    public void setDescendents(java.util.ArrayList t,
                               C45PruneableClassifierTreeG originalLeaf)

  add the grafted nodes at originalLeaf's position in tree. a recursive function that terminates when t is empty.

  Parameters:
    `t` - the list of nodes to graft
    `originalLeaf` - the leaf that the grafts are replacing

### biprob

    public double biprob(double x,
                         double n,
                         double r)
                  throws java.lang.Exception

  Significance test

  Parameters:
    `double` - x, double n, double r.
  Returns:
    returns the probability of obtaining x or MORE out of n if r proportion of n are positive. z for normal estimation of binomial probability of obtaining x or more out of n, if r proportion of n are positive
  Throws:
    `java.lang.Exception`

### toString

    public java.lang.String toString()

  Prints tree structure.

  Overrides:
    `toString` in class `ClassifierTree`
  Returns:
    the tree structure

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

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

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