Package

weka.classifiers.meta

Synopsis

This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier.
Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data.

For more information, see:
Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.

Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (dagging).

Options

The table below describes the options available for Dagging.

Option	Description
classifier	The base classifier to be used.
debug	If set to true, classifier may output additional info to the console.
numFolds	The number of folds to use for splitting the training set into smaller chunks for the base classifier.
seed	The random number seed to be used.
verbose	Whether to ouput some additional information during building.

Capabilities

The table below describes the capabilites of Dagging.

Capability	Supported
Class	Nominal class, Missing class values, Binary class
Attributes	Numeric attributes, Empty nominal attributes, Binary attributes, Nominal attributes, Unary attributes, Missing values
Min # of instances	1

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