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.

License	GNU General Public License 3
Categories	Weka Business Logic Libraries Machine Learning
GroupId	nz.ac.waikato.cms.weka
ArtifactId	paceRegression
Last Version	1.0.2
Release Date	Apr 26, 2012
Type	jar
Description	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.
Project URL	http://weka.sourceforge.net/doc.packages/paceRegression
Project Organization	University of Waikato, Hamilton, NZ

Download paceRegression

Filename	Size
paceRegression-1.0.2.pom
paceRegression-1.0.2.jar	36 KB
paceRegression-1.0.2-tests.jar	4 KB
paceRegression-1.0.2-test-sources.jar	1 KB
paceRegression-1.0.2-sources.jar	28 KB
paceRegression-1.0.2-javadoc.jar	82 KB

How to add to project

Apache Maven

<!-- https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/paceRegression/ -->
<dependency>
    <groupId>nz.ac.waikato.cms.weka</groupId>
    <artifactId>paceRegression</artifactId>
    <version>1.0.2</version>
</dependency>

Gradle Groovy

// https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/paceRegression/
implementation 'nz.ac.waikato.cms.weka:paceRegression:1.0.2'

Gradle Kotlin

// https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/paceRegression/
implementation ("nz.ac.waikato.cms.weka:paceRegression:1.0.2")

Apache Buildr

'nz.ac.waikato.cms.weka:paceRegression:jar:1.0.2'

Apache Ivy

<dependency org="nz.ac.waikato.cms.weka" name="paceRegression" rev="1.0.2">
  <artifact name="paceRegression" type="jar" />
</dependency>

Groovy Grape

@Grapes(
@Grab(group='nz.ac.waikato.cms.weka', module='paceRegression', version='1.0.2')
)

Scala SBT

libraryDependencies += "nz.ac.waikato.cms.weka" % "paceRegression" % "1.0.2"

Leiningen

[nz.ac.waikato.cms.weka/paceRegression "1.0.2"]

Dependencies

compile (1)

Group / Artifact	Type	Version
nz.ac.waikato.cms.weka : weka-dev	jar	[3.7.1,)

test (2)

Group / Artifact	Type	Version
nz.ac.waikato.cms.weka : weka-dev	test-jar	[3.7.1,)
junit : junit	jar	3.8.2

Project Modules

There are no modules declared in this project.

Versions

Version
1.0.2 Apr 26, 2012
1.0.1 Apr 24, 2012