- 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.