Tells a sub-ResultProducer to reproduce the current
run for varying sized subsamples of the dataset. Normally used with an
AveragingResultProducer and CrossValidationResultProducer combo to generate
learning curve results. For non-numeric result fields, the first value is
used.
Valid options are:
-X <num steps>
The number of steps in the learning rate curve.
(default 10)
-W <class name>
The full class name of a ResultProducer.
eg: weka.experiment.CrossValidationResultProducer
Options specific to result producer weka.experiment.AveragingResultProducer:
-F <field name>
The name of the field to average over.
(default "Fold")
-X <num results>
The number of results expected per average.
(default 10)
-S
Calculate standard deviations.
(default only averages)
-W <class name>
The full class name of a ResultProducer.
eg: weka.experiment.CrossValidationResultProducer
Options specific to result producer weka.experiment.CrossValidationResultProducer:
-X <number of folds>
The number of folds to use for the cross-validation.
(default 10)
-D
Save raw split evaluator output.
-O <file/directory name/path>
The filename where raw output will be stored.
If a directory name is specified then then individual
outputs will be gzipped, otherwise all output will be
zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
-W <class name>
The full class name of a SplitEvaluator.
eg: weka.experiment.ClassifierSplitEvaluator
Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
-W <class name>
The full class name of the classifier.
eg: weka.classifiers.bayes.NaiveBayes
-C <index>
The index of the class for which IR statistics
are to be output. (default 1)
-I <index>
The index of an attribute to output in the
results. This attribute should identify an
instance in order to know which instances are
in the test set of a cross validation. if 0
no output (default 0).
-P
Add target and prediction columns to the result
for each fold.
Options specific to classifier weka.classifiers.rules.ZeroR:
-D
If set, classifier is run in debug mode and
may output additional info to the console
All options after -- will be passed to the result producer.