Abstract
Cross-Conformal Prediction (CCP) is a recently proposed approach for overcoming the computational inefficiency problem of Conformal Prediction (CP) without sacrificing as much informational efficiency as Inductive Conformal Prediction (ICP). In effect CCP is a hybrid approach combining the ideas of cross-validation and ICP. In the case of classification the predictions of CCP have been shown to be empirically valid and more informationally efficient than those of the ICP. This paper introduces CCP in the regression setting and examines its empirical validity and informational efficiency compared to that of the original CP and ICP when combined with Ridge Regression.
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Papadopoulos, H. (2015). Cross-Conformal Prediction with Ridge Regression. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_21
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DOI: https://doi.org/10.1007/978-3-319-17091-6_21
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