Abstract
Optimal bins describe continuous predictor variables in the form of best fit categories for making predictions, e.g., about families at high risk of’ bank loan defaults. In addition, it can be used for, e.g., predicting health risk cut-offs about individual future families, based on their characteristics (Chap. 56).
This chapter was previously published in “Machine learning in medicine-cookbook 2” as Chap. 2, 2014.
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
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Cleophas, T.J., Zwinderman, A.H. (2015). Predicting High-Risk-Bin Memberships (1,445 Families). In: Machine Learning in Medicine - a Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-319-15195-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-15195-3_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15194-6
Online ISBN: 978-3-319-15195-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)