Abstract
Optimal binning is a so-called non-metric method for describing a continuous predictor variable in the form of best fit categories for making predictions. Like binary partitioning (Machine Learning in Medicine Part One, Binary partitioning, pp. 79–86, Springer Heidelberg, Germany, 2013) it uses an exact test called the entropy method, which is based on log likelihoods. It may, therefore, produce better statistics than traditional tests. In addition, unnecessary noise due to continuous scaling is deleted, and categories for identifying patients at high risk of particular outcomes can be identified. This chapter is to assess its efficiency in medical research.
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Cleophas, T.J., Zwinderman, A.H. (2014). Optimal Binning for Finding High Risk Cut-offs (1445 Families). In: Machine Learning in Medicine - Cookbook. SpringerBriefs in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-04181-0_19
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DOI: https://doi.org/10.1007/978-3-319-04181-0_19
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-04181-0
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