Package

weka.classifiers.meta

Synopsis

This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces.

For more information, see

Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL http://citeseer.ist.psu.edu/ho98random.html.

Options

The table below describes the options available for RandomSubSpace.

Option	Description
classifier	The base classifier to be used.
debug	If set to true, classifier may output additional info to the console.
numIterations	The number of iterations to be performed.
seed	The random number seed to be used.
subSpaceSize	Size of each subSpace: if less than 1 as a percentage of the number of attributes, otherwise the absolute number of attributes.

Capabilities

The table below describes the capabilites of RandomSubSpace.

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

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