Class for doing classification using regression methods. Class is binarized and one regression model is built for each class value. For more information, see, for example E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
(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, Nominal class, Binary class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Missing class values, Only multi-Instance data] min # Instance: 1
D: If set, classifier is run in debug mode and may output additional info to the console
W: Full name of base classifier. (default: weka.classifiers.trees.M5P)
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)
L: Save instances at the nodes in the tree (for visualization purposes)
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