weka.classifiers.trees.j48Consolidated
public class C45ConsolidatedSplit extends weka.classifiers.trees.j48.C45Split
Modifier and Type | Field and Description |
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private static long |
serialVersionUID
for serialization
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Constructor and Description |
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C45ConsolidatedSplit(int attIndex,
int minNoObj,
double sumOfWeights,
boolean useMDLcorrection,
weka.core.Instances data,
double splitPointConsolidated)
Creates a split model to be used to consolidate the decision around the set of samples,
but with a null distribution
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C45ConsolidatedSplit(int attIndex,
int minNoObj,
double sumOfWeights,
boolean useMDLcorrection,
weka.core.Instances data,
weka.core.Instances[] samplesVector,
double splitPointConsolidated)
Creates a split model based on the consolidated decision
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attIndex, buildClassifier, classProb, codingCost, gainRatio, getRevision, infoGain, leftSide, minsAndMaxs, resetDistribution, rightSide, setSplitPoint, sourceExpression, splitPoint, weights, whichSubset
private static final long serialVersionUID
public C45ConsolidatedSplit(int attIndex, int minNoObj, double sumOfWeights, boolean useMDLcorrection, weka.core.Instances data, double splitPointConsolidated)
attIndex
- attribute to split onminNoObj
- minimum number of objectssumOfWeights
- sum of the weightsdata
- the training sample. Only to get information about the attributessplitPointConsolidated
- the split point to use to split, if numerical.public C45ConsolidatedSplit(int attIndex, int minNoObj, double sumOfWeights, boolean useMDLcorrection, weka.core.Instances data, weka.core.Instances[] samplesVector, double splitPointConsolidated) throws java.lang.Exception
attIndex
- attribute to split onminNoObj
- minimum number of objectssumOfWeights
- sum of the weightsdata
- the training sample. Only to get information about the attributessamplesVector
- the vector of samples used for consolidationsplitPointConsolidated
- the split point to use to split, if numerical.java.lang.Exception
- if something goes wrong