RandomSubSpace
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 |