AdditiveRegression
Package
weka.classifiers.meta
Synopsis
Meta classifier that enhances the performance of a regression base classifier. Each iteration fits a model to the residuals left by the classifier on the previous iteration. Prediction is accomplished by adding the predictions of each classifier. Reducing the shrinkage (learning rate) parameter helps prevent overfitting and has a smoothing effect but increases the learning time.
For more information see:
J.H. Friedman (1999). Stochastic Gradient Boosting.
Options
The table below describes the options available for AdditiveRegression.
Option |
Description |
|---|---|
classifier |
The base classifier to be used. |
debug |
If set to true, classifier may output additional info to the console. |
numIterations |
The number of iterations to be performed. |
shrinkage |
Shrinkage rate. Smaller values help prevent overfitting and have a smoothing effect (but increase learning time). Default = 1.0, ie. no shrinkage. |
Capabilities
The table below describes the capabilites of AdditiveRegression.
Capability |
Supported |
|---|---|
Class |
Numeric class, Date class, Missing class values |
Attributes |
Numeric attributes, Binary attributes, Empty nominal attributes, Date attributes, Unary attributes, Nominal attributes, Missing values |
Min # of instances |
1 |