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| Authors: | Niki Zumbrunnen, Lutz Dümbgen | ||||
| Title: | pvclass: An R Package for p Values for Classification | ||||
| Abstract: | Let (X, Y) be a random variable consisting of an observed feature vector X and an unobserved class label Y ∈ {1, 2, . . . , L} with unknown joint distribution. In addition, let D be a training data set consisting of n completely observed independent copies of (X, Y). Instead of providing point predictors (classifiers) for Y , we compute for each b ∈ {1, 2, . . . , L} a p value π_b (X, D) for the null hypothesis that Y = b, treating Y temporarily as a fixed parameter, i.e., we construct a prediction region for Y with a certain confidence. The advantages of this approach over more traditional ones are reviewed briefly. In principle, any reasonable classifier can be modified to yield nonparametric p values. We describe the R package pvclass which computes nonparametric p values for the potential class memberships of new observations as well as cross-validated p values for the training data. Additionally, it provides graphical displays and quantitative analyses of the p values. | ||||
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Page views:: 1867. Submitted: 2014-07-04. Published: 2017-06-05. |
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| Paper: |
pvclass: An R Package for p Values for Classification
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| DOI: |
10.18637/jss.v078.i04
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This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |