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Computes marginal conformal p-values using conformal prediction in binary classification tasks. Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) <https://proceedings.mlr.press/v204/tyagi23a.html>.
Package details |
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Author | Chhavi Tyagi [aut, cre] |
Maintainer | Chhavi Tyagi <tyagi.chhavi2222@gmail.com> |
License | MIT + file LICENSE |
Version | 0.1.0 |
Package repository | View on CRAN |
Installation |
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