Abstract
Testing a model is called validation. When validation is done on the same dataset that was used to develop the model, it is called internal validation. Because of the risk of overfitting, internal validation may lead to false-positive reassurance about a model. The strongest form of validation is external validation, where the model is tested on a dataset very different from the one it was developed on.
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Hussan H, et al. Utility of machine learning in develo** a predictive model for early-age-onset colorectal neoplasia using electronic health records. PLoS One. 2022;17(3):e0265209.
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Cohen, A.L. (2023). Validation. In: Problems and Pitfalls in Medical Literature. Springer, Cham. https://doi.org/10.1007/978-3-031-40295-1_13
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DOI: https://doi.org/10.1007/978-3-031-40295-1_13
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