Synopsis#
The Class-Relevance Method. (The generalised, multi-target version of the Binary Relevance (BR) method).
Options#
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-W <classifier name>
Full name of base classifier. (default: weka.classifiers.trees.J48)
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-output-debug-info
If set, classifier is run in debug mode and may output additional info to the console
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-do-not-check-capabilities
If set, classifier capabilities are not checked before classifier is built (use with caution).
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-num-decimal-places
The number of decimal places for the output of numbers in the model (default 2).
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-batch-size
The desired batch size for batch prediction (default 100).
Options specific to classifier weka.classifiers.trees.J48:
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-U
Use unpruned tree.
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-O
Do 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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-R
Use 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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-B
Use binary splits only.
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-S
Do not perform subtree raising.
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-L
Do not clean up after the tree has been built.
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-A
Laplace smoothing for predicted probabilities.
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-J
Do 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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-doNotMakeSplitPointActualValue
Do not make split point actual value.
-
-output-debug-info
If set, classifier is run in debug mode and may output additional info to the console
-
-do-not-check-capabilities
If set, classifier capabilities are not checked before classifier is built (use with caution).
-
-num-decimal-places
The number of decimal places for the output of numbers in the model (default 2).
-
-batch-size
The desired batch size for batch prediction (default 100).