Abstract
Binary partitioning is used to determine the best fit decision cut-off levels for a dataset with false positive and false negative patients. It serves a purpose similar to that of the receiver operating characteristic (ROC) curve method, but, unlike ROC curves, it is adjusted for the magnitude of the samples, and therefore more precise.
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Cleophas, T.J., Zwinderman, A.H. (2012). Binary Partitioning for CART (Classification and Regression Tree) Methods. In: Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2. SpringerBriefs in Statistics. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4704-3_16
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DOI: https://doi.org/10.1007/978-94-007-4704-3_16
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Publisher Name: Springer, Dordrecht
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Online ISBN: 978-94-007-4704-3
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