Flow
weka.RandomizableFilteredClassifier

weka.RandomizableFilteredClassifier

Visibility: public Uploaded 12-04-2018 by Blazej Manczak Weka_3.8.2 0 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Weka implementation of RandomizableFilteredClassifier

Parameters

-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. default: "weka.filters.unsupervised.attribute.RandomProjection -N 10 -D Sparse1"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 instead of just skipping them.
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.lazy.IBk
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).
doNotCheckForModifiedClassAttributeIf set, classifier will not check whether the filter modifies the class (use with caution).
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

0
Runs

List all runs
Parameter:
Rendering chart
Rendering table