java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.trees.m5.M5Base

All Implemented Interfaces:

Serializable, Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler

Direct Known Subclasses:

M5P, M5Rules
```
public abstract class M5Base
extends AbstractClassifier
implements AdditionalMeasureProducer, TechnicalInformationHandler
```
M5Base. Implements base routines for generating M5 Model trees and rules.
The original algorithm M5 was invented by Quinlan:
Quinlan J. R. (1992). Learning with continuous classes. Proceedings of the Australian Joint Conference on Artificial Intelligence. 343--348. World Scientific, Singapore.
Yong Wang made improvements and created M5':
Wang, Y and Witten, I. H. (1997). Induction of model trees for predicting continuous classes. Proceedings of the poster papers of the European Conference on Machine Learning. University of Economics, Faculty of Informatics and Statistics, Prague.
Valid options are:
-U
Use unsmoothed predictions.
-R
Build regression tree/rule rather than model tree/rule

Version:

$Revision$

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor Description

M5Base()
Constructor

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`buildClassifier(Instances data)`	Generates the classifier.
`String`	`buildRegressionTreeTipText()`	Returns the tip text for this property
`double`	`classifyInstance(Instance inst)`	Calculates a prediction for an instance using a set of rules or an M5 model tree
`Enumeration<String>`	`enumerateMeasures()`	Returns an enumeration of the additional measure names
`String`	`generateRulesTipText()`	Returns the tip text for this property
`boolean`	`getBuildRegressionTree()`	Get the value of regressionTree.
`Capabilities`	`getCapabilities()`	Returns default capabilities of the classifier, i.e., of LinearRegression.
`boolean`	`getKuenzelEtAlSplitSelection()`	Get whether Kuenzel et al.'s split selection is used
`RuleNode`	`getM5RootNode()`
`double`	`getMeasure(String additionalMeasureName)`	Returns the value of the named measure
`double`	`getMinNumInstances()`	Get the minimum number of instances to allow at a leaf node
`String[]`	`getOptions()`	Gets the current settings of the classifier.
`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`	`getUnpruned()`	Get whether unpruned tree/rules are being generated
`boolean`	`getUseUnsmoothed()`	Get whether or not smoothing is being used
`String`	`kuenzelEtAlSplitSelectionTipText()`	Returns the tip text for this property
`Enumeration<Option>`	`listOptions()`	Returns an enumeration describing the available options
`double`	`measureNumRules()`	return the number of rules
`String`	`minNumInstancesTipText()`	Returns the tip text for this property
`void`	`setBuildRegressionTree(boolean newregressionTree)`	Set the value of regressionTree.
`void`	`setKuenzelEtAlSplitSelection(boolean unpruned)`	Use Kuenzel et al.'s split selection
`void`	`setMinNumInstances(double minNum)`	Set the minimum number of instances to allow at a leaf node
`void`	`setOptions(String[] options)`	Parses a given list of options.
`void`	`setUnpruned(boolean unpruned)`	Use unpruned tree/rules
`void`	`setUseUnsmoothed(boolean s)`	Use unsmoothed predictions
`String`	`toString()`	Returns a description of the classifier
`String`	`unprunedTipText()`	Returns the tip text for this property
`String`	`useUnsmoothedTipText()`	Returns the tip text for this property

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

- Constructor Detail
  - M5Base
```
public M5Base()
```
    Constructor
- Method Detail
  - 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
  - listOptions
```
public Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    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:
    -U
    Use unsmoothed predictions.
    -R
    Build a regression tree rather than a model tree.
    
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - kuenzelEtAlSplitSelectionTipText
```
public String kuenzelEtAlSplitSelectionTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setKuenzelEtAlSplitSelection
```
public void setKuenzelEtAlSplitSelection(boolean unpruned)
```
    Use Kuenzel et al.'s split selection
    
    Parameters:
    
    unpruned - true if Kuenzel et al.'s split selection is used
  - getKuenzelEtAlSplitSelection
```
public boolean getKuenzelEtAlSplitSelection()
```
    Get whether Kuenzel et al.'s split selection is used
    
    Returns:
    
    true if Kuenzel et al.'s split selection is used
  - unprunedTipText
```
public String unprunedTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - 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
  - generateRulesTipText
```
public String generateRulesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - useUnsmoothedTipText
```
public String useUnsmoothedTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseUnsmoothed
```
public void setUseUnsmoothed(boolean s)
```
    Use unsmoothed predictions
    
    Parameters:
    
    s - true if unsmoothed predictions are to be used
  - getUseUnsmoothed
```
public boolean getUseUnsmoothed()
```
    Get whether or not smoothing is being used
    
    Returns:
    
    true if unsmoothed predictions are to be used
  - buildRegressionTreeTipText
```
public String buildRegressionTreeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getBuildRegressionTree
```
public boolean getBuildRegressionTree()
```
    Get the value of regressionTree.
    
    Returns:
    
    Value of regressionTree.
  - setBuildRegressionTree
```
public void setBuildRegressionTree(boolean newregressionTree)
```
    Set the value of regressionTree.
    
    Parameters:
    
    newregressionTree - Value to assign to regressionTree.
  - minNumInstancesTipText
```
public String minNumInstancesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinNumInstances
```
public void setMinNumInstances(double minNum)
```
    Set the minimum 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
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier, i.e., of LinearRegression.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - set of instances serving as training data
    
    Throws:
    
    Exception - if the classifier has not been generated successfully
  - classifyInstance
```
public double classifyInstance(Instance inst)
                        throws Exception
```
    Calculates a prediction for an instance using a set of rules or an M5 model tree
    
    Specified by:
    
    classifyInstance in interface Classifier
    
    Overrides:
    
    classifyInstance in class AbstractClassifier
    
    Parameters:
    
    inst - the instance whos class value is to be predicted
    
    Returns:
    
    the prediction
    
    Throws:
    
    Exception - if a prediction can't be made.
  - toString
```
public String toString()
```
    Returns a description of the classifier
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a description of the classifier as a String
  - enumerateMeasures
```
public Enumeration<String> 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(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:
    
    Exception - if the named measure is not supported
  - measureNumRules
```
public double measureNumRules()
```
    return the number of rules
    
    Returns:
    
    the number of rules (same as # linear models & # leaves in the tree)
  - getM5RootNode
```
public RuleNode getM5RootNode()
```

Class M5Base

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Constructor Detail

M5Base

Method Detail

getTechnicalInformation

listOptions

setOptions

getOptions

kuenzelEtAlSplitSelectionTipText

setKuenzelEtAlSplitSelection

getKuenzelEtAlSplitSelection

unprunedTipText

setUnpruned

getUnpruned

generateRulesTipText

useUnsmoothedTipText

setUseUnsmoothed

getUseUnsmoothed

buildRegressionTreeTipText

getBuildRegressionTree

setBuildRegressionTree

minNumInstancesTipText

setMinNumInstances

getMinNumInstances

getCapabilities

buildClassifier

classifyInstance

toString

enumerateMeasures

getMeasure

measureNumRules

getM5RootNode