Class for generating a PART decision list
Uses separate-and-conquer.Builds a partial C4.5 decision tree in each iteration and makes the "best" leaf into a rule.
For more information, see:
Eibe Frank, Ian H.
Witten: Generating Accurate Rule Sets Without Global Optimization.In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
(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.
C: Set confidence threshold for pruning. (default 0.25)
M: Set minimum number of objects per leaf. (default 2)
R: Use reduced error pruning.
N: Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)
B: Use binary splits only.
U: Generate unpruned decision list.
J: Do not use MDL correction for info gain on numeric attributes.
Q: Seed for random data shuffling (default 1).
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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