Generates a decision list for regression problems using separate-and-conquer. In each iteration it builds a model tree using M5 and makes the "best" leaf into a rule. For more information see: Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, 1-12, 1999. Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992. Y. Wang, I. H. Witten: Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning, 1997.
(based on WEKA 3.6)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
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, Numeric class, Date class, Missing class values] Dependencies:  min # Instance: 1
N: Use unpruned tree/rules
U: Use unsmoothed predictions
R: Build regression tree/rule rather than a model tree/rule
M: Set minimum number of instances per leaf (default 4)
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To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.6) from the below update site following our NodePit Product and Node Installation Guide:
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