Description Usage Arguments Details References Examples
Give on-line information about available control options for Weka learners or filters and their R interfaces.
1 | WOW(x)
|
x |
a character string giving either the fully qualified name of a Weka learner or filter class in JNI notation, or the name of an available R interface, or an object obtained from applying these interfaces to build an associator, classifier, clusterer, or filter. |
See list_Weka_interfaces
for the available interface
functions.
K. Hornik, C. Buchta, and A. Zeileis (2009). Open-source machine learning: R meets Weka. Computational Statistics, 24/2, 225–232. doi: 10.1007/s00180-008-0119-7.
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OpenJDK 64-Bit Server VM warning: Can't detect initial thread stack location - find_vma failed
-U Use unpruned tree.
-O Do not collapse tree.
-C <pruning confidence>
Set confidence threshold for pruning. (default 0.25)
Number of arguments: 1.
-M <minimum number of instances>
Set minimum number of instances per leaf. (default 2)
Number of arguments: 1.
-R Use reduced error pruning.
-N <number of folds>
Set number of folds for reduced error pruning. One fold is used
as pruning set. (default 3)
Number of arguments: 1.
-B Use binary splits only.
-S Do not perform subtree raising.
-L Do not clean up after the tree has been built.
-A Laplace smoothing for predicted probabilities.
-J Do not use MDL correction for info gain on numeric attributes.
-Q <seed>
Seed for random data shuffling (default 1).
Number of arguments: 1.
-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).
Number of arguments: 1.
-batch-size
The desired batch size for batch prediction (default 100).
Number of arguments: 1.
-K Use kernel density estimator rather than normal distribution
for numeric attributes
-D Use supervised discretization to process numeric attributes
-O Display model in old format (good when there are many classes)
-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).
Number of arguments: 1.
-batch-size
The desired batch size for batch prediction (default 100).
Number of arguments: 1.
Warning message:
system call failed: Cannot allocate memory
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