WrapperSubsetEval (original) (raw)

WrapperSubsetEval:

Evaluates attribute sets by using a learning scheme. Cross validation is used to estimate the accuracy of the learning scheme for a set of attributes.

For more information see:

Ron Kohavi, George H. John (1997). Wrappers for feature subset selection. Artificial Intelligence. 97(1-2):273-324.

BibTeX:

@article{Kohavi1997, author = {Ron Kohavi and George H. John}, journal = {Artificial Intelligence}, note = {Special issue on relevance}, number = {1-2}, pages = {273-324}, title = {Wrappers for feature subset selection}, volume = {97}, year = {1997}, ISSN = {0004-3702} }

Valid options are:

-B class name of base learner to use for accuracy estimation. Place any classifier options LAST on the command line following a "--". eg.: -B weka.classifiers.bayes.NaiveBayes ... -- -K (default: weka.classifiers.rules.ZeroR)

-F number of cross validation folds to use for estimating accuracy. (default=5)

-R Seed for cross validation accuracy testimation. (default = 1)

-T threshold by which to execute another cross validation (standard deviation---expressed as a percentage of the mean). (default: 0.01 (1%))

Options specific to scheme weka.classifiers.rules.ZeroR:

-D If set, classifier is run in debug mode and may output additional info to the console