Estimation of prediction error by using <Emphasis Type="Italic">K</Emphasis>-fold cross-validation |
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Authors: | Tadayoshi Fushiki |
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Institution: | (1) Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China |
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Abstract: | Estimation of prediction accuracy is important when our aim is prediction. The training error is an easy estimate of prediction
error, but it has a downward bias. On the other hand, K-fold cross-validation has an upward bias. The upward bias may be negligible in leave-one-out cross-validation, but it sometimes
cannot be neglected in 5-fold or 10-fold cross-validation, which are favored from a computational standpoint. Since the training
error has a downward bias and K-fold cross-validation has an upward bias, there will be an appropriate estimate in a family that connects the two estimates.
In this paper, we investigate two families that connect the training error and K-fold cross-validation. |
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Keywords: | |
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