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## weka.classifiers.trees.m5
Class Rule

    java.lang.Object
      weka.classifiers.trees.m5.Rule

All Implemented Interfaces:
  java.io.Serializable

    public class Rule

extends java.lang.Object
implements java.io.Serializable

Generates a single m5 tree or rule

Version:
  $Revision: 1.10.2.1 $
Author:
  Mark Hall
See Also:
  Serialized Form

Constructor Summary
`Rule()` Constructor declaration

Method Summary
`void` | `buildClassifier(Instances data)` Generates a single rule or m5 model tree.
`double` | `classifyInstance(Instance instance)` Calculates a prediction for an instance using this rule or M5 model tree
`RuleNode` | `getM5RootNode()`
`double` | `getMinNumInstances()` Get the minimum number of instances to allow at a leaf node
`boolean` | `getRegressionTree()` Get the value of regressionTree.
`boolean` | `getSmoothing()` Get whether or not smoothing has been turned on
`boolean` | `getUnpruned()` Get whether unpruned tree/rules are being generated
`boolean` | `getUseTree()` get whether an m5 tree is being used rather than rules
`Instances` | `notCoveredInstances()` Get the instances not covered by this rule
`void` | `setMinNumInstances(double minNum)` Set the minumum number of instances to allow at a leaf node
`void` | `setRegressionTree(boolean newregressionTree)` Set the value of regressionTree.
`void` | `setSmoothing(boolean s)` Smooth predictions
`void` | `setUnpruned(boolean unpruned)` Use unpruned tree/rules
`void` | `setUseTree(boolean u)` Use an m5 tree rather than generate rules
`RuleNode` | `topOfTree()` Returns the top of the tree.
`java.lang.String` | `toString()` Return a description of the m5 tree or rule

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

Constructor Detail

### Rule

    public Rule()

  Constructor declaration

Method Detail

### buildClassifier

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

  Generates a single rule or m5 model tree.

  Parameters:
    `data` - set of instances serving as training data
  Throws:
    `java.lang.Exception` - if the rule has not been generated successfully

### classifyInstance

    public double classifyInstance(Instance instance)
                            throws java.lang.Exception

  Calculates a prediction for an instance using this rule or M5 model tree

  Parameters:
    `inst` - the instance whos class value is to be predicted
  Returns:
    the prediction
  Throws:
    `if` - a prediction can't be made.
    `java.lang.Exception`

### topOfTree

    public RuleNode topOfTree()

  Returns the top of the tree.

### toString

    public java.lang.String toString()

  Return a description of the m5 tree or rule

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a description of the m5 tree or rule as a String

### setUnpruned

    public void setUnpruned(boolean unpruned)

  Use unpruned tree/rules

  Parameters:
    `unpruned` - true if unpruned tree/rules are to be generated

### getUnpruned

    public boolean getUnpruned()

  Get whether unpruned tree/rules are being generated

  Returns:
    true if unpruned tree/rules are to be generated

### setUseTree

    public void setUseTree(boolean u)

  Use an m5 tree rather than generate rules

  Parameters:
    `u` - true if m5 tree is to be used

### getUseTree

    public boolean getUseTree()

  get whether an m5 tree is being used rather than rules

  Returns:
    true if an m5 tree is being used.

### setSmoothing

    public void setSmoothing(boolean s)

  Smooth predictions

  Parameters:
    `s` - true if smoothing is to be used

### getSmoothing

    public boolean getSmoothing()

  Get whether or not smoothing has been turned on

  Returns:
    true if smoothing is being used

### notCoveredInstances

    public Instances notCoveredInstances()

  Get the instances not covered by this rule

  Returns:
    the instances not covered

### getRegressionTree

    public boolean getRegressionTree()

  Get the value of regressionTree.

  Returns:
    Value of regressionTree.

### setRegressionTree

    public void setRegressionTree(boolean newregressionTree)

  Set the value of regressionTree.

  Parameters:
    `newregressionTree` - Value to assign to regressionTree.

### setMinNumInstances

    public void setMinNumInstances(double minNum)

  Set the minumum number of instances to allow at a leaf node

  Parameters:
    `minNum` - the minimum number of instances

### getMinNumInstances

    public double getMinNumInstances()

  Get the minimum number of instances to allow at a leaf node

  Returns:
    a `double` value

### getM5RootNode

    public RuleNode getM5RootNode()

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