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 <base learner>
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 <num>
number of cross validation folds to use for estimating accuracy.
(default=5)
-R <seed>
Seed for cross validation accuracy testimation.
(default = 1)
-T <num>
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
author:
Mark Hall (mhall@cs.waikato.ac.nz)
version:
$Revision: 1.29 $ | 