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m_train
weka.core.Instances m_train
Training data.
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m_Successors
SimpleCart[] m_Successors
Successor nodes.
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m_Attribute
weka.core.Attribute m_Attribute
Attribute used to split data.
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m_SplitValue
double m_SplitValue
Split point for a numeric attribute.
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m_SplitString
java.lang.String m_SplitString
Split subset used to split data for nominal attributes.
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m_ClassValue
double m_ClassValue
Class value if the node is leaf.
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m_ClassAttribute
weka.core.Attribute m_ClassAttribute
Class attriubte of data.
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m_minNumObj
double m_minNumObj
Minimum number of instances in at the terminal nodes.
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m_numFoldsPruning
int m_numFoldsPruning
Number of folds for minimal cost-complexity pruning.
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m_Alpha
double m_Alpha
Alpha-value (for pruning) at the node.
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m_numIncorrectModel
double m_numIncorrectModel
Number of training examples misclassified by the model (subtree rooted).
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m_numIncorrectTree
double m_numIncorrectTree
Number of training examples misclassified by the model (subtree not
rooted).
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m_isLeaf
boolean m_isLeaf
Indicate if the node is a leaf node.
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m_Prune
boolean m_Prune
If use minimal cost-compexity pruning.
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m_totalTrainInstances
int m_totalTrainInstances
Total number of instances used to build the classifier.
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m_Props
double[] m_Props
Proportion for each branch.
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m_ClassProbs
double[] m_ClassProbs
Class probabilities.
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m_Distribution
double[] m_Distribution
Distributions of leaf node (or temporary leaf node in minimal
cost-complexity pruning)
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m_Heuristic
boolean m_Heuristic
If use huristic search for nominal attributes in multi-class problems
(default true).
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m_UseOneSE
boolean m_UseOneSE
If use the 1SE rule to make final decision tree.
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m_SizePer
double m_SizePer
Training data size.