Visibility: public Uploaded 19-04-2017 by Freek Rooks Weka_3.8.1 15 runs
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  • Verified_Supervised_Classification weka weka_3.8.1
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Weka implementation of RandomizableFilteredClassifier


Wweka.RandomTree(14)Full name of base classifier. (default: weka.classifiers.lazy.IBk)


-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
AThe nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
DThe distribution to use for calculating the random matrix. Sparse1 is: sqrt(3)*{-1 with prob(1/6), 0 with prob(2/3), +1 with prob(1/6)} Sparse2 is: {-1 with prob(1/2), +1 with prob(1/2)}
EMinimise mean squared error rather than mean absolute error when using -X option with numeric prediction.
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.RandomProjection -N 10 -R 42 -D Sparse1
IWeight neighbours by the inverse of their distance (use when k > 1)
KNumber of nearest neighbours (k) used in classification. (Default = 1)
MReplace missing values using the ReplaceMissingValues filter
NThe number of dimensions (attributes) the data should be reduced to (default 10; exclusive of the class attribute, if it is set).
PThe percentage of dimensions (attributes) the data should be reduced to (exclusive of the class attribute, if it is set). The -N option is ignored if this option is present and is greater than zero.
RThe random seed for the random number generator used for calculating the random matrix (default 42).
SRandom number seed. (default 1)default: 1
WFull name of base classifier. (default: weka.classifiers.lazy.IBk)default: weka.classifiers.trees.RandomTree
XSelect the number of nearest neighbours between 1 and the k value specified using hold-one-out evaluation on the training data (use when k > 1)
batch-sizeThe desired batch size for batch prediction (default 100).
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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