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Article
Logistic Ensemble Models
Grey Literature from PhD Candidates
  • Bob Vanderheyden, Kennesaw State University
  • Jennifer L. Priestley, Kennesaw State University
Department
Statistics and Analytical Sciences
Submission Date
1-1-2017
Abstract

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., improvements in basic credit behavior must result in improved credit worthiness scores). Machine Learning technologies provide very good performance with minimal analyst intervention, so they are well suited to a high volume analytic environment but the majority are “black box” tools that provide very limited insight or interpretability into key drivers of model performance or predicted model output values. This paper presents a methodology that blends one of the most popular predictive statistical modeling methods with a core model enhancement strategy, found in machine learning. The resulting prediction methodology provides solid performance, from minimal analyst effort, while providing the interpretability and rationality, required in regulated industries.

Citation Information
Bob Vanderheyden and Jennifer L. Priestley. "Logistic Ensemble Models" (2017)
Available at: http://works.bepress.com/jennifer_priestley/31/