l

leastMedSquared

Implements a least median squared linear regression utilizing 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.
http://weka.sourceforge.net/doc.packages/leastMedSquared
GNU General Public License 3
University of Waikato, Hamilton, NZ
Tony Voyle
model predictions squared basis meadian least data existing regression annick chosen from algorithm leroy outlier final functions robust linear with form error peter weka random class linearregression median detection 1987 generated rousseeuw subsamples implements lowest utilizing
Files download
File Operation
leastMedSquared-1.0.2.jar download
leastMedSquared-1.0.2.pom download
leastMedSquared-1.0.2-sources.jar download
Apache Maven 
<dependency>
  <groupId>nz.ac.waikato.cms.weka</groupId>
  <artifactId>leastMedSquared</artifactId>
  <version>1.0.2</version>
</dependency>
Gradle Groovy 
implementation 'nz.ac.waikato.cms.weka:leastMedSquared:1.0.2'
Gradle Kotlin 
implementation("nz.ac.waikato.cms.weka:leastMedSquared:1.0.2")
Scala SBT 
libraryDependencies += "nz.ac.waikato.cms.weka" % "leastMedSquared" % "1.0.2"
Groovy Grape 
@Grapes(
  @Grab(group='nz.ac.waikato.cms.weka', module='leastMedSquared', version='1.0.2')
)
Apache Ivy 
<dependency org="nz.ac.waikato.cms.weka" name="leastMedSquared" rev="1.0.2" />
Leiningen 
[nz.ac.waikato.cms.weka/leastMedSquared "1.0.2"]
Apache Buildr 
'nz.ac.waikato.cms.weka:leastMedSquared:jar:1.0.2'
Dependencies 
<parent>
  <groupId>org.sonatype.oss</groupId>
  <artifactId>oss-parent</artifactId>
  <version>7</version>
</parent>

compile

|-- nz.ac.waikato.cms.weka:weka-dev

test

|-- nz.ac.waikato.cms.weka:weka-dev

|-- junit:junit:3.8.2