Synopsis#
The Fourclass Pairwise (FW) method. Trains a multi-class base classifier for each pair of labels -- (L*(L-1))/2 in total --, each with four possible class values: {00,01,10,11} representing the possible combinations of relevant (1) /irrelevant (0) for the pair. Uses a voting + threshold scheme at testing time where e.g., 01 from pair jk gives one vote to label k; any label with votes above the threshold is considered relevant.
Options#
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-W <classifier name>Full name of base classifier. (default: weka.classifiers.trees.J48)
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-output-debug-infoIf set, classifier is run in debug mode and may output additional info to the console
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-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
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-num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).
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-batch-sizeThe desired batch size for batch prediction (default 100).
Options specific to classifier weka.classifiers.trees.J48:
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-UUse unpruned tree.
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-ODo not collapse tree.
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-C <pruning confidence>Set confidence threshold for pruning. (default 0.25)
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-M <minimum number of instances>Set minimum number of instances per leaf. (default 2)
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-RUse reduced error pruning.
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-N <number of folds>Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)
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-BUse binary splits only.
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-SDo not perform subtree raising.
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-LDo not clean up after the tree has been built.
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-ALaplace smoothing for predicted probabilities.
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-JDo not use MDL correction for info gain on numeric attributes.
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-Q <seed>Seed for random data shuffling (default 1).
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-doNotMakeSplitPointActualValueDo not make split point actual value.
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-output-debug-infoIf set, classifier is run in debug mode and may output additional info to the console
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-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
-
-num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).
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-batch-sizeThe desired batch size for batch prediction (default 100).