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weka.FilteredClassifier_Standardize_NaiveBayes

weka.FilteredClassifier_Standardize_NaiveBayes

Visibility: public Uploaded 14-02-2015 by Manuel Martin Salvador Weka_3.7.12 1216 runs
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  • study_1 study_41 study_73 Verified_Learning_Curve,Verified_Supervised_Classification weka weka_3.7.12
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Weka implementation of FilteredClassifier

Components

Wweka.NaiveBayes(10)Full name of base classifier. (default: weka.classifiers.trees.J48)
Fweka.Standardize(1)Full class name of filter to use, followed by filter options. eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"

Parameters

-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 binary attributes for discretized attributes.
EUse better encoding of split point for MDL.
FFull class name of filter to use, followed by filter options. eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"default: weka.filters.unsupervised.attribute.Standardize
JDo not use MDL correction for info gain on numeric attributes.
KUse Kononenko's MDL criterion.
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.
QSeed for random data shuffling (default 1).
RUse reduced error pruning.
SDo not perform subtree raising.
UUse unpruned tree.
VInvert matching sense of column indexes.
WFull name of base classifier. (default: weka.classifiers.trees.J48)default: weka.classifiers.bayes.NaiveBayes
YUse bin numbers rather than ranges for discretized attributes.
output-debug-infoIf set, classifier is run in debug mode and may output additional info to the console
precisionPrecision for bin boundary labels. (default = 6 decimal places).

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