3 papers
stat.AP2018
A Descriptive Study of Variable Discretization and Cost-Sensitive Logistic Regression on Imbalanced Credit Data
Lili Zhang, Herman Ray, Jennifer Priestley +1
Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive l…
stat.ML2018
Logistic Ensemble Models
Bob Vanderheyden, Jennifer Priestley
Predictive models that are developed in a regulated industry or a regulated application, like determination of credit worthiness, must be interpretable and rational (e.g., meaningf…
stat.ML2018
Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models
Lili Zhang, Jennifer Priestley, Xuelei Ni
In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discri…