Download all versions of extraTrees JAR files with all dependencies
extraTrees from group nz.ac.waikato.cms.weka (version 1.0.2)
Package for generating a single Extra-Tree. Use with the RandomCommittee meta classifier to generate an Extra-Trees forest for classification or regression. This classifier requires all predictors to be numeric. Missing values are not allowed. Instance weights are taken into account. For more information, see Pierre Geurts, Damien Ernst, Louis Wehenkel (2006). Extremely randomized trees. Machine Learning. 63(1):3-42.
Artifact extraTrees
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 03. December 2017
Tags: allowed predictors 2006 numeric single generate wehenkel package regression pierre meta missing more values ernst tree randomized taken instance louis extremely damien information with extra randomcommittee into generating forest learning machine requires weights classification geurts classifier trees account this
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/extraTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 03. December 2017
Tags: allowed predictors 2006 numeric single generate wehenkel package regression pierre meta missing more values ernst tree randomized taken instance louis extremely damien information with extra randomcommittee into generating forest learning machine requires weights classification geurts classifier trees account this
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/extraTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
extraTrees from group nz.ac.waikato.cms.weka (version 1.0.1)
Package for generating a single Extra-Tree. Use with the RandomCommittee meta classifier to generate an Extra-Trees forest for classification or regression. This classifier requires all predictors to be numeric. Missing values are not allowed. Instance weights are taken into account. For more information, see Pierre Geurts, Damien Ernst, Louis Wehenkel (2006). Extremely randomized trees. Machine Learning. 63(1):3-42.
Artifact extraTrees
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 30. April 2014
Tags: allowed predictors 2006 numeric single generate wehenkel package regression pierre meta missing more values ernst tree randomized taken instance louis extremely damien information with extra randomcommittee into generating forest learning machine requires weights classification geurts classifier trees account this
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/extraTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 30. April 2014
Tags: allowed predictors 2006 numeric single generate wehenkel package regression pierre meta missing more values ernst tree randomized taken instance louis extremely damien information with extra randomcommittee into generating forest learning machine requires weights classification geurts classifier trees account this
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/extraTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
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