Visibility: public Uploaded 15-04-2016 by Martijn Post Weka_3.7.13 454 runs
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  • Verified_Supervised_Classification weka weka_3.7.13
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Weka implementation of AttributeSelectedClassifier


Wweka.RandomForest(9)Full name of base classifier. (default: weka.classifiers.trees.J48)
Sweka.Ranker(3)Full class name of search method, followed by its options. eg: "weka.attributeSelection.BestFirst -D 1" (default weka.attributeSelection.BestFirst)
Eweka.InfoGainAttributeEval(2)Full class name of attribute evaluator, followed by its options. eg: "weka.attributeSelection.CfsSubsetEval -L" (default weka.attributeSelection.CfsSubsetEval)


-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
-doNotMakeSplitPointActualValueDo not make split point actual value.
ALaplace smoothing for predicted probabilities.
BUse binary splits only.
CSet confidence threshold for pruning. (default 0.25)
DOutput debugging info.
EFull class name of attribute evaluator, followed by its options. eg: "weka.attributeSelection.CfsSubsetEval -L" (default weka.attributeSelection.CfsSubsetEval)default: weka.attributeSelection.InfoGainAttributeEval
JDo not use MDL correction for info gain on numeric attributes.
LDo not clean up after the tree has been built.
MSet minimum number of instances per leaf. (default 2)
NSet number of folds for reduced error pruning. One fold is used as pruning set. (default 3)
ODo not collapse tree.
PThe size of the thread pool, for example, the number of cores in the CPU. (default 1)
QSeed for random data shuffling (default 1).
RUse reduced error pruning.
SFull class name of search method, followed by its options. eg: "weka.attributeSelection.BestFirst -D 1" (default weka.attributeSelection.BestFirst)default: weka.attributeSelection.Ranker
UUse unpruned tree.
WFull name of base classifier. (default: weka.classifiers.trees.J48)default: weka.classifiers.trees.RandomForest
ZPrecompute the full correlation matrix at the outset, rather than compute correlations lazily (as needed) during the search. Use this in conjuction with parallel processing in order to speed up a backward search.
num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).
output-debug-infoIf set, classifier is run in debug mode and may output additional info to the console


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