Selection of Transformations of Continuous Predictors in Logistic Regression
Document Type
Article
Publication Date
4-14-2018
Publication Title
Advances in Intelligent Systems and Computing
Volume
738
First page number:
443
Last page number:
447
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
Data mining; F1; Machine learning; Precision; Recal
Disciplines
Artificial Intelligence and Robotics | Computer Sciences | Technology and Innovation
Repository Citation
Chang, M.,
Dalpatadu, R. J.,
Singh, A. K.
(2018).
Selection of Transformations of Continuous Predictors in Logistic Regression.
Advances in Intelligent Systems and Computing, 738
443-447.
http://dx.doi.org/10.1007/978-3-319-77028-4_58