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M

m_attIndex - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
Attribute to split on.
m_chiSquaredProb - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
ChiSquared probability of split.
m_chiSquaredProb - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
ChiSquared probability of split.
m_CHminNumObjSplit - Variable in class weka.classifiers.trees.JCHAID
Minimum number of instances to split a node
m_CHordinalAtts - Variable in class weka.classifiers.trees.JCHAID
Stores which attributes are ordinals (monotonic predictors) (based on the member m_DiscretizeCols of weka.filters.supervised.attribute.Discretize class)
m_CHsearchBestSplit - Variable in class weka.classifiers.trees.JCHAID
Indicates if the quest of the best binary split will be done, after merging 3 or more categories This process could add a considerable latency and that is why it is optional.
m_CHsigLevelAtt - Variable in class weka.classifiers.trees.JCHAID
Significance level for the selection of the attribute to split a node.
m_CHsigLevelMergeSplit - Variable in class weka.classifiers.trees.JCHAID
Significance level for the quest of the best combination of the categories of an attribute
m_complexityIndex - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
Desired number of branches.
m_distri_orig - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
Saves a copy of the original distribution
m_hasMissingValues - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
Indicates if there are missing values or not
m_index - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
Number of split points.
m_indicators - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
The indicators used to map the old values.
m_minNoObj - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
Minimum number of objects in a split.
m_minNoObj - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
Minimum number of objects in a split.
m_minNumObjSplit - Variable in class weka.classifiers.trees.jchaid.CHAIDModelSelection
Minimum number of instances to split a node
m_missingCurrentIndex - Variable in class weka.classifiers.trees.jchaidstar.CHAIDStarSplit
Current position for missing values on the attribute to split on.
m_missingIdx - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
Original position for missing values on the attribute to split on.
m_missingOriginalIdx - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
Index of Missing values in its original position
m_numBagsNotEmpty - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
Indicates the number of bags not empty in the initial distribution
m_ordered - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
Indicate if the nature of the categories is ordered, that is to say, if the values have to be merged with contiguous categories (Ordinal attributes) or any grouping of categories is permissible (Nominal attributes)
m_ordered - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
Indicate if the nature of the categories is ordered Default: false, that is, Attribute.ORDERING_SYMBOLIC
m_ordinalAtts - Variable in class weka.classifiers.trees.jchaid.CHAIDModelSelection
Stores which attributes are ordinals (monotonic predictors) (based on the member m_DiscretizeCols of weka.filters.supervised.attribute.Discretize class)
m_searchBestSplit - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
Indicates if the quest of the best binary split will be done, after merging 3 or more categories This process could add a considerable latency and that is why it is optional.
m_searchBestSplit - Variable in class weka.classifiers.trees.jchaid.CHAIDModelSelection
Indicates if the quest of the best binary split will be done, after merging 3 or more categories This process could add a considerable latency and that is why it is optional.
m_searchBestSplit - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
Indicates if the quest of the best binary split will be done, after merging 3 or more categories This process could add a considerable latency and that is why it is optional.
m_sigLevelAtt - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
Set the significance level for the selection of the attribute to split a node.
m_sigLevelAtt - Variable in class weka.classifiers.trees.jchaid.CHAIDModelSelection
Significance level for the selection of the attribute to split a node.
m_sigLevelAtt - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
Set the significance level for the selection of the attribute to split a node.
m_sigLevelMergeSplit - Variable in class weka.classifiers.trees.jchaid.CHAIDDistribution
Set the significance level for the quest of the best combination of the categories of an attribute
m_sigLevelMergeSplit - Variable in class weka.classifiers.trees.jchaid.CHAIDModelSelection
Significance level for the quest of the best combination of the categories of an attribute
m_sigLevelMergeSplit - Variable in class weka.classifiers.trees.jchaid.CHAIDSplit
Set the significance level for the quest of the best combination of the categories of an attribute
m_splitPoint - Variable in class weka.classifiers.trees.jchaidstar.CHAIDStarSplit
Value of split point.
m_XRFFUsed - Variable in class weka.classifiers.trees.JCHAID
Indicates if XRFF format was used
main(String[]) - Static method in class weka.classifiers.trees.JCHAID
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.JCHAIDStar
Main method for testing this class
mergeAll() - Method in class weka.classifiers.trees.jchaid.CHAIDDistribution
Merge all bags into only one bag
mergeCategories(int, int) - Method in class weka.classifiers.trees.jchaid.CHAIDDistribution
Merges the rows of two categories; always the second one over the first one
mergeIndicators(int, int) - Method in class weka.classifiers.trees.jchaid.CHAIDDistribution
Updates membership indicators after merging two categories; always the second one over the first one
mergeSmallGroups() - Method in class weka.classifiers.trees.jchaid.CHAIDDistribution
Merges any category having fewer observations than the specification for the minimum subgroup size with the most similar other category, as measured by the smallest pairwise chi-square
mergeValues() - Method in class weka.classifiers.trees.jchaid.CHAIDDistribution
Merges values based on CHAID algorithm and returns list of subset indicators for the values.
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