Package | Description |
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weka.classifiers.trees |
Modifier and Type | Field and Description |
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protected BFTree[] |
BFTree.m_Successors
Successor nodes.
|
Modifier and Type | Method and Description |
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protected FastVector |
BFTree.computeSplitInfo(BFTree node,
Instances data,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[][] props,
double[][] totalSubsetWeights,
boolean useHeuristic,
boolean useGini)
Compute the best splitting attribute, split point or subset and the best
gini gain or iformation gain for a given dataset.
|
protected void |
BFTree.makeTree(FastVector BestFirstElements,
BFTree root,
Instances train,
Instances test,
FastVector modelError,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[] classProbs,
double totalWeight,
double[] branchProps,
int minNumObj,
boolean useHeuristic,
boolean useGini,
boolean useErrorRate)
This method is to find the number of expansions based on internal
cross-validation for just post-pruning.
|
protected boolean |
BFTree.makeTree(FastVector BestFirstElements,
BFTree root,
Instances train,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[] classProbs,
double totalWeight,
double[] branchProps,
int minNumObj,
boolean useHeuristic,
boolean useGini)
This method is to find the number of expansions based on internal
cross-validation for just pre-pruning.
|
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