Package weka.classifiers.trees
Class M5P
java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.trees.m5.M5Base
weka.classifiers.trees.M5P
- All Implemented Interfaces:
Serializable,Cloneable,Classifier,AdditionalMeasureProducer,BatchPredictor,CapabilitiesHandler,CapabilitiesIgnorer,CommandlineRunnable,Drawable,OptionHandler,RevisionHandler,TechnicalInformationHandler
M5Base. Implements base routines for generating M5
Model trees and rules
The original algorithm M5 was invented by R. Quinlan and Yong Wang made improvements.
For more information see:
Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.
Y. Wang, I. H. Witten: Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning, 1997. BibTeX:
The original algorithm M5 was invented by R. Quinlan and Yong Wang made improvements.
For more information see:
Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.
Y. Wang, I. H. Witten: Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning, 1997. BibTeX:
@inproceedings{Quinlan1992,
address = {Singapore},
author = {Ross J. Quinlan},
booktitle = {5th Australian Joint Conference on Artificial Intelligence},
pages = {343-348},
publisher = {World Scientific},
title = {Learning with Continuous Classes},
year = {1992}
}
@inproceedings{Wang1997,
author = {Y. Wang and I. H. Witten},
booktitle = {Poster papers of the 9th European Conference on Machine Learning},
publisher = {Springer},
title = {Induction of model trees for predicting continuous classes},
year = {1997}
}
Valid options are:
-N Use unpruned tree/rules
-U Use unsmoothed predictions
-R Build regression tree/rule rather than a model tree/rule
-M <minimum number of instances> Set minimum number of instances per leaf (default 4)
-L Save instances at the nodes in the tree (for visualization purposes)
- Version:
- $Revision: 14534 $
- Author:
- Mark Hall
- See Also:
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Field Summary
Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULTFields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionString[]Gets the current settings of the classifier.Returns the revision string.booleanGet whether instance data is being save.returns information about the classifiergraph()Return a dot style String describing the tree.intReturns the type of graph this classifier represents.Returns an enumeration describing the available optionsstatic voidMain method by which this class can be testedReturns the tip text for this propertyvoidsetOptions(String[] options) Parses a given list of options.voidsetSaveInstances(boolean save) Set whether to save instance data at each node in the tree for visualization purposesMethods inherited from class weka.classifiers.trees.m5.M5Base
buildClassifier, buildRegressionTreeTipText, classifyInstance, enumerateMeasures, generateRulesTipText, getBuildRegressionTree, getCapabilities, getM5RootNode, getMeasure, getMinNumInstances, getTechnicalInformation, getUnpruned, getUseUnsmoothed, measureNumRules, minNumInstancesTipText, setBuildRegressionTree, setMinNumInstances, setUnpruned, setUseUnsmoothed, toString, unprunedTipText, useUnsmoothedTipTextMethods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
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Constructor Details
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M5P
public M5P()Creates a newM5Pinstance.
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Method Details
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globalInfo
returns information about the classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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graphType
public int graphType()Returns the type of graph this classifier represents. -
graph
Return a dot style String describing the tree. -
saveInstancesTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setSaveInstances
public void setSaveInstances(boolean save) Set whether to save instance data at each node in the tree for visualization purposes- Parameters:
save- abooleanvalue
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getSaveInstances
public boolean getSaveInstances()Get whether instance data is being save.- Returns:
- a
booleanvalue
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listOptions
Returns an enumeration describing the available options- Specified by:
listOptionsin interfaceOptionHandler- Overrides:
listOptionsin classM5Base- Returns:
- an enumeration of all the available options
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setOptions
Parses a given list of options. Valid options are:-N Use unpruned tree/rules
-U Use unsmoothed predictions
-R Build regression tree/rule rather than a model tree/rule
-M <minimum number of instances> Set minimum number of instances per leaf (default 4)
-L Save instances at the nodes in the tree (for visualization purposes)
- Specified by:
setOptionsin interfaceOptionHandler- Overrides:
setOptionsin classM5Base- Parameters:
options- the list of options as an array of strings- Throws:
Exception- if an option is not supported
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getOptions
Gets the current settings of the classifier.- Specified by:
getOptionsin interfaceOptionHandler- Overrides:
getOptionsin classM5Base- Returns:
- an array of strings suitable for passing to setOptions
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getRevision
Returns the revision string.- Specified by:
getRevisionin interfaceRevisionHandler- Overrides:
getRevisionin classAbstractClassifier- Returns:
- the revision
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main
Main method by which this class can be tested- Parameters:
args- an array of options
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