Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves. The algorithm can deal with binary and multi-class target variables, numeric and nominal attributes and missing values. For more information see: Joao Gama (2004). Functional Trees. Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.
(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:  min # Instance: 1
B: Binary splits (convert nominal attributes to binary ones)
P: Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.
I: Set fixed number of iterations for LogitBoost (instead of using cross-validation)
F: Set Funtional Tree type to be generate: 0 for FT, 1 for FTLeaves and 2 for FTInner
M: Set minimum number of instances at which a node can be split (default 15)
W: Set beta for weight trimming for LogitBoost. Set to 0 (default) for no weight trimming.
A: The AIC is used to choose the best iteration.
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A zipped version of the software site can be downloaded here.
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