FT
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 |