Class for constructing a tree that considers K randomly chosen attributes at each node
Performs no pruning.Also has an option to allow estimation of class probabilities based on a hold-out set (backfitting).
(based on WEKA 3.7)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
K: Number of attributes to randomly investigate (<0 = int(log_2(#attributes)+1)).
M: Set minimum number of instances per leaf.
S: Seed for random number generator. (default 1)
depth: The maximum depth of the tree, 0 for unlimited. (default 0)
N: Number of folds for backfitting (default 0, no backfitting).
U: Allow unclassified instances.
D: If set, classifier is run in debug mode and may output additional info to the console
The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies:  min # Instance: 1
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
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A zipped version of the software site can be downloaded here.
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