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, Chap. 7, 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.
This chapter was previously published in “Machine learning in medicine-cookbook 1” as Chap.19, 2013.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Electronic Supplementary Material(s)
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Cleophas, T.J., Zwinderman, A.H. (2020). Optimal Binning for Finding High Risk Cut-offs (1445 Families). In: Machine Learning in Medicine – A Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-030-33970-8_61
Download citation
DOI: https://doi.org/10.1007/978-3-030-33970-8_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33969-2
Online ISBN: 978-3-030-33970-8
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)