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paceRegression

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.
http://weka.sourceforge.net/doc.packages/paceRegression
GNU General Public License 3
University of Waikato, Hamilton, NZ
Yong Wang
pace using handle tends implmentation 2002 regression optimal nominal where used missing more values coefficients when consists overall conference work information spaces nineteenth provably dimensional international under class number machine models conditions that them australia proceedings case estimators this small binary theory witten wang instances infinity current zealand probability regularity sydney linear building prediction learning threshold fitting therefore either implementation modeling approach hamilton attributes certain high group also 2000
Files download
File Operation
paceRegression-1.0.1.jar download
paceRegression-1.0.1.pom download
paceRegression-1.0.1-sources.jar download
Apache Maven 
<dependency>
  <groupId>nz.ac.waikato.cms.weka</groupId>
  <artifactId>paceRegression</artifactId>
  <version>1.0.1</version>
</dependency>
Gradle Groovy 
implementation 'nz.ac.waikato.cms.weka:paceRegression:1.0.1'
Gradle Kotlin 
implementation("nz.ac.waikato.cms.weka:paceRegression:1.0.1")
Scala SBT 
libraryDependencies += "nz.ac.waikato.cms.weka" % "paceRegression" % "1.0.1"
Groovy Grape 
@Grapes(
  @Grab(group='nz.ac.waikato.cms.weka', module='paceRegression', version='1.0.1')
)
Apache Ivy 
<dependency org="nz.ac.waikato.cms.weka" name="paceRegression" rev="1.0.1" />
Leiningen 
[nz.ac.waikato.cms.weka/paceRegression "1.0.1"]
Apache Buildr 
'nz.ac.waikato.cms.weka:paceRegression:jar:1.0.1'
Dependencies 
<parent>
  <groupId>org.sonatype.oss</groupId>
  <artifactId>oss-parent</artifactId>
  <version>7</version>
</parent>

compile

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

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

test

|-- junit:junit:3.8.2