LeastMedSq
Package
weka.classifiers.functions
Synopsis
Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model.
The basis of the algorithm is
Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection.
Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (leastMedSquared).
Options
The table below describes the options available for LeastMedSq.
Option |
Description |
|---|---|
debug |
If set to true, classifier may output additional info to the console. |
randomSeed |
Set the seed for selecting random subsamples of the training data. |
sampleSize |
Set the size of the random samples used to generate the least sqaured regression functions. |
Capabilities
The table below describes the capabilites of LeastMedSq.
Capability |
Supported |
|---|---|
Class |
Date class, Numeric class, Missing class values |
Attributes |
Binary attributes, Nominal attributes, Numeric attributes, Unary attributes, Missing values, Date attributes, Empty nominal attributes |
Min # of instances |
1 |