Logistic Ensemble Models

TL;DR

This paper introduces a logistic ensemble modeling approach that combines traditional statistical methods with machine learning enhancements to achieve high performance and interpretability in regulated industry applications.

Contribution

It presents a novel methodology blending logistic regression with machine learning strategies to improve interpretability and performance in binary classification tasks.

Findings

Achieves high predictive performance with minimal analyst effort.

Provides interpretable models suitable for regulated industries.

Balances accuracy and interpretability effectively.

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., meaningful improvements in basic credit behavior must result in improved credit worthiness scores). Machine Learning technologies provide very good performance with minimal analyst intervention, making them 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 for binary classification 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, as well as in other environments where interpretation of model parameters is required (e.g. businesses that require interpretation of models, to take action on them).