Package

weka.classifiers.trees

Synopsis

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.

Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (functionalTrees).

Options

The table below describes the options available for FT.

Option	Description
binSplit	Convert all nominal attributes to binary ones before building the tree. This means that all splits in the final tree will be binary.
debug	If set to true, classifier may output additional info to the console.
errorOnProbabilities	Minimize error on probabilities instead of misclassification error when cross-validating the number of LogitBoost iterations. When set, the number of LogitBoost iterations is chosen that minimizes the root mean squared error instead of the misclassification error.
minNumInstances	Set the minimum number of instances at which a node is considered for splitting. The default value is 15.
modelType	The type of FT model. 0, for FT, 1, for FTLeaves, and 2, for FTInner
numBoostingIterations	Set a fixed number of iterations for LogitBoost. If >= 0, this sets a fixed number of LogitBoost iterations that is used everywhere in the tree. If < 0, the number is cross-validated.
useAIC	The AIC is used to determine when to stop LogitBoost iterations. The default is not to use AIC.
weightTrimBeta	Set the beta value used for weight trimming in LogitBoost. Only instances carrying (1 - beta)% of the weight from previous iteration are used in the next iteration. Set to 0 for no weight trimming. The default value is 0.

Capabilities

The table below describes the capabilites of FT.

Capability	Supported
Class	Nominal class, Binary class, Missing class values
Attributes	Empty nominal attributes, Unary attributes, Nominal attributes, Binary attributes, Date attributes, Missing values, Numeric attributes
Min # of instances	1

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