Fast decision tree learner
Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting).Only sorts values for numeric attributes once.
Missing values are dealt with by splitting the corresponding instances into pieces (i.e.as in C4.5).
(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.
M: Set minimum number of instances per leaf (default 2).
V: Set minimum numeric class variance proportion of train variance for split (default 1e-3).
N: Number of folds for reduced error pruning (default 3).
S: Seed for random data shuffling (default 1).
P: No pruning.
L: Maximum tree depth (default -1, no maximum)
I: Initial class value count (default 0)
R: Spread initial count over all class values (i.e. don't use 1 per value)
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, Numeric class, Date 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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