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java.lang.Objectweka.classifiers.AbstractClassifier
weka.classifiers.trees.REPTree
public class REPTree
Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting). Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5).
Valid options are:-M <minimum number of instances> Set minimum number of instances per leaf (default 2).
-V <minimum variance for split> Set minimum numeric class variance proportion of train variance for split (default 1e-3).
-N <number of folds> Number of folds for reduced error pruning (default 3).
-S <seed> Seed for random data shuffling (default 1).
-P No pruning.
-L Maximum tree depth (default -1, no maximum)
Field Summary |
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Fields inherited from interface weka.core.Drawable |
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BayesNet, Newick, NOT_DRAWABLE, TREE |
Constructor Summary | |
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REPTree()
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Method Summary | |
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void |
buildClassifier(Instances data)
Builds classifier. |
double[] |
distributionForInstance(Instance instance)
Computes class distribution of an instance using the tree. |
java.util.Enumeration |
enumerateMeasures()
Returns an enumeration of the additional measure names. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
int |
getMaxDepth()
Get the value of MaxDepth. |
double |
getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure. |
double |
getMinNum()
Get the value of MinNum. |
double |
getMinVarianceProp()
Get the value of MinVarianceProp. |
boolean |
getNoPruning()
Get the value of NoPruning. |
int |
getNumFolds()
Get the value of NumFolds. |
java.lang.String[] |
getOptions()
Gets options from this classifier. |
java.lang.String |
getRevision()
Returns the revision string. |
int |
getSeed()
Get the value of Seed. |
java.lang.String |
globalInfo()
Returns a string describing classifier |
java.lang.String |
graph()
Outputs the decision tree as a graph |
int |
graphType()
Returns the type of graph this classifier represents. |
java.util.Enumeration |
listOptions()
Lists the command-line options for this classifier. |
static void |
main(java.lang.String[] argv)
Main method for this class. |
java.lang.String |
maxDepthTipText()
Returns the tip text for this property |
java.lang.String |
minNumTipText()
Returns the tip text for this property |
java.lang.String |
minVariancePropTipText()
Returns the tip text for this property |
java.lang.String |
noPruningTipText()
Returns the tip text for this property |
java.lang.String |
numFoldsTipText()
Returns the tip text for this property |
int |
numNodes()
Computes size of the tree. |
java.lang.String |
seedTipText()
Returns the tip text for this property |
void |
setMaxDepth(int newMaxDepth)
Set the value of MaxDepth. |
void |
setMinNum(double newMinNum)
Set the value of MinNum. |
void |
setMinVarianceProp(double newMinVarianceProp)
Set the value of MinVarianceProp. |
void |
setNoPruning(boolean newNoPruning)
Set the value of NoPruning. |
void |
setNumFolds(int newNumFolds)
Set the value of NumFolds. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setSeed(int newSeed)
Set the value of Seed. |
java.lang.String |
toSource(java.lang.String className)
Returns the tree as if-then statements. |
java.lang.String |
toString()
Outputs the decision tree. |
Methods inherited from class weka.classifiers.AbstractClassifier |
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classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public REPTree()
Method Detail |
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public java.lang.String globalInfo()
public java.lang.String noPruningTipText()
public boolean getNoPruning()
public void setNoPruning(boolean newNoPruning)
newNoPruning
- Value to assign to NoPruning.public java.lang.String minNumTipText()
public double getMinNum()
public void setMinNum(double newMinNum)
newMinNum
- Value to assign to MinNum.public java.lang.String minVariancePropTipText()
public double getMinVarianceProp()
public void setMinVarianceProp(double newMinVarianceProp)
newMinVarianceProp
- Value to assign to MinVarianceProp.public java.lang.String seedTipText()
public int getSeed()
public void setSeed(int newSeed)
newSeed
- Value to assign to Seed.public java.lang.String numFoldsTipText()
public int getNumFolds()
public void setNumFolds(int newNumFolds)
newNumFolds
- Value to assign to NumFolds.public java.lang.String maxDepthTipText()
public int getMaxDepth()
public void setMaxDepth(int newMaxDepth)
newMaxDepth
- Value to assign to MaxDepth.public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class AbstractClassifier
public java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class AbstractClassifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-M <minimum number of instances> Set minimum number of instances per leaf (default 2).
-V <minimum variance for split> Set minimum numeric class variance proportion of train variance for split (default 1e-3).
-N <number of folds> Number of folds for reduced error pruning (default 3).
-S <seed> Seed for random data shuffling (default 1).
-P No pruning.
-L Maximum tree depth (default -1, no maximum)
setOptions
in interface OptionHandler
setOptions
in class AbstractClassifier
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic int numNodes()
public java.util.Enumeration enumerateMeasures()
enumerateMeasures
in interface AdditionalMeasureProducer
public double getMeasure(java.lang.String additionalMeasureName)
getMeasure
in interface AdditionalMeasureProducer
additionalMeasureName
- the name of the measure to query for its value
java.lang.IllegalArgumentException
- if the named measure is not supportedpublic Capabilities getCapabilities()
getCapabilities
in interface Classifier
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class AbstractClassifier
Capabilities
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in interface Classifier
data
- the data to train with
java.lang.Exception
- if building failspublic double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in interface Classifier
distributionForInstance
in class AbstractClassifier
instance
- the instance to compute the distribution for
java.lang.Exception
- if computation failspublic java.lang.String toSource(java.lang.String className) throws java.lang.Exception
toSource
in interface Sourcable
className
- the name for the generated class
java.lang.Exception
- if something goes wrongpublic int graphType()
graphType
in interface Drawable
public java.lang.String graph() throws java.lang.Exception
graph
in interface Drawable
java.lang.Exception
- if generation failspublic java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class AbstractClassifier
public static void main(java.lang.String[] argv)
argv
- the commandline options
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