Selection of Transformations of Continuous Predictors in Logistic Regression

  • Michael Chang
  • Rohan J. Dalpatadu
  • Ashok K. Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)

Abstract

The binary logistic regression is a machine learning tool for classification and discrimination that is widely used in business analytics and medical research. Transforming continuous predictors to improve model performance of logistic regression is a common practice, but no systematic method for finding optimal transformations exists in the statistical or data mining literature. In this paper, the problem of selecting transformations of continuous predictors to improve the performance of logistic regression models is considered. The proposed method is based upon the point-biserial correlation coefficient between the binary response and a continuous predictor. Several examples are presented to illustrate the proposed method.

Keywords

Machine learning Data mining Precision Recal F1 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Michael Chang
    • 1
  • Rohan J. Dalpatadu
    • 1
  • Ashok K. Singh
    • 2
  1. 1.Department of Mathematical SciencesUniversity of Nevada, Las VegasLas VegasUSA
  2. 2.William F. Harrah College of Hotel AdministrationUniversity of Nevada, Las VegasLas VegasUSA

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