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m_Folds
int m_Folds
The number of folds to split data into Grow and Prune for REP
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m_ClassAttribute
Attribute m_ClassAttribute
The class attribute of the data
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m_Antds
FastVector<E> m_Antds
The vector of antecedents of this rule
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m_DefDstr
double[] m_DefDstr
The default rule distribution of the data not covered
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m_Cnsqt
double[] m_Cnsqt
The consequent of this rule
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m_NumClasses
int m_NumClasses
Number of classes in the training data
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m_Seed
long m_Seed
The seed to perform randomization
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m_Random
java.util.Random m_Random
The Random object used for randomization
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m_Targets
FastVector<E> m_Targets
The predicted classes recorded for each antecedent in the growing data
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m_IsExclude
boolean m_IsExclude
Whether to use exlusive expressions for nominal attributes
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m_MinNo
double m_MinNo
The minimal number of instance weights within a split
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m_NumAntds
int m_NumAntds
The number of antecedents in pre-pruning