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M

m_bestGoodness - Variable in class weka.attributeSelection.IWSS
Array which keeps the best cross-validation so far in the incremental search
m_dataset - Variable in class weka.attributeSelection.IWSS
The dataset
m_formerSelected - Variable in class weka.attributeSelection.IWSS
Number of former attributes in ranking to be straight-forward selected before running the incremental search.
m_minFolds - Variable in class weka.attributeSelection.IWSS
Minimum number of folds in inner cross-validation whose wrapper goodness must be improved when selecting a new attribute.
m_numFolds - Static variable in class weka.attributeSelection.IWSS
Number of folds in the inner cross-validation
m_numSelected - Variable in class weka.attributeSelection.IWSS
Number of selected attributes (first in ranking is selected by default)
m_rankingMetric - Variable in class weka.attributeSelection.IWSS
Single Attribute evaluator used to create the ranking
m_replaceSelection - Variable in class weka.attributeSelection.IWSS
Flag to activate the option of testing, at each step of the incremental search, the swapping of a selected feature with another not selected yet.
m_selected - Variable in class weka.attributeSelection.IWSS
Selected attributes
m_skipRanking - Variable in class weka.attributeSelection.IWSS
It is set to true if IWSS.setStartSet is called
m_testData - Variable in class weka.attributeSelection.IWSS
The test set for inner cross validation
m_theta - Variable in class weka.attributeSelection.IWSS
Value for early stopping: (0-1] percentage of remaining attributes to visit.
m_trainingData - Variable in class weka.attributeSelection.IWSS
The training set for inner cross validation
minFoldsTipText() - Method in class weka.attributeSelection.IWSS
Returns the tip text for this property
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