Class RandomizableFilteredClassifier

All Implemented Interfaces:
Serializable, Cloneable, Classifier, IterativeClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, WeightedAttributesHandler, WeightedInstancesHandler

public class RandomizableFilteredClassifier extends FilteredClassifier
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.

Valid options are:

 -F <filter specification>
  Full class name of filter to use, followed
  by filter options.
  default: "weka.filters.unsupervised.attribute.RandomProjection -N 10 -D Sparse1"
  If set, classifier is run in debug mode and
  may output additional info to the console
  Full name of base classifier.
  (default: weka.classifiers.lazy.IBk)
 -S num
 Set the random number seed (default 1). 
 Options specific to classifier weka.classifiers.lazy.IBk:
  Weight neighbours by the inverse of their distance
  (use when k > 1)
  Weight neighbours by 1 - their distance
  (use when k > 1)
 -K <number of neighbors>
  Number of nearest neighbours (k) used in classification.
  (Default = 1)
  Minimise mean squared error rather than mean absolute
  error when using -X option with numeric prediction.
 -W <window size>
  Maximum number of training instances maintained.
  Training instances are dropped FIFO. (Default = no window)
  Select 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)
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
$Revision: 9117 $
Eibe Frank
See Also:
  • Constructor Details

    • RandomizableFilteredClassifier

      public RandomizableFilteredClassifier()
      Default constructor.
  • Method Details