ClassifierAttributeEval :
Evaluates the worth of an attribute by using a user-specified classifier.
Valid options are:
-L
Evaluate an attribute by measuring the impact of leaving it out
from the full set instead of considering its worth in isolation
-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%))
-E <acc | rmse | mae | f-meas | auc | auprc>
Performance evaluation measure to use for selecting attributes.
(Default = accuracy for discrete class and rmse for numeric class)
-IRclass <label | index>
Optional class value (label or 1-based index) to use in conjunction with
IR statistics (f-meas, auc or auprc). Omitting this option will use
the class-weighted average.
Options specific to scheme weka.classifiers.rules.ZeroR:
-output-debug-info
If set, classifier is run in debug mode and
may output additional info to the console
-do-not-check-capabilities
If set, classifier capabilities are not checked before classifier is built
(use with caution).
-execution-slots <integer>
Number of attributes to evaluate in parallel.
Default = 1 (i.e. no parallelism)