Download all versions of thresholdSelector JAR files with all dependencies
thresholdSelector from group nz.ac.waikato.cms.weka (version 1.0.3)
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. The midpoint threshold is set so that a given performance measure is optimized. Currently this is the F-measure. Performance is measured either on the training data, a hold-out set or using cross-validation. In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).
Artifact thresholdSelector
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 25. April 2014
Tags: using measure range have data currently selecting training midpoint normally given scheme optimized reside performance useful probability between learner validation hold measured expanded addition threshold metaclassifier that point either narrow base will classifier produces probabilities output their this returned cross very
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/thresholdSelector
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 25. April 2014
Tags: using measure range have data currently selecting training midpoint normally given scheme optimized reside performance useful probability between learner validation hold measured expanded addition threshold metaclassifier that point either narrow base will classifier produces probabilities output their this returned cross very
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/thresholdSelector
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
thresholdSelector from group nz.ac.waikato.cms.weka (version 1.0.2)
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. The midpoint threshold is set so that a given performance measure is optimized. Currently this is the F-measure. Performance is measured either on the training data, a hold-out set or using cross-validation. In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).
Artifact thresholdSelector
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: using measure range have data currently selecting training midpoint normally given scheme optimized reside performance useful probability between learner validation hold measured expanded addition threshold metaclassifier that point either narrow base will classifier produces probabilities output their this returned cross very
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/thresholdSelector
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 26. April 2012
Tags: using measure range have data currently selecting training midpoint normally given scheme optimized reside performance useful probability between learner validation hold measured expanded addition threshold metaclassifier that point either narrow base will classifier produces probabilities output their this returned cross very
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/thresholdSelector
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
thresholdSelector from group nz.ac.waikato.cms.weka (version 1.0.1)
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. The midpoint threshold is set so that a given performance measure is optimized. Currently this is the F-measure. Performance is measured either on the training data, a hold-out set or using cross-validation. In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).
Artifact thresholdSelector
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 24. April 2012
Tags: using measure range have data currently selecting training midpoint normally given scheme optimized reside performance useful probability between learner validation hold measured expanded addition threshold metaclassifier that point either narrow base will classifier produces probabilities output their this returned cross very
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/thresholdSelector
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 24. April 2012
Tags: using measure range have data currently selecting training midpoint normally given scheme optimized reside performance useful probability between learner validation hold measured expanded addition threshold metaclassifier that point either narrow base will classifier produces probabilities output their this returned cross very
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/thresholdSelector
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
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