Logistic Regression Through the Veil of Imprecise Data

TL;DR

This paper introduces an imprecise logistic regression model that incorporates data uncertainties, allowing for a more honest representation of epistemic uncertainty in predictive modeling.

Contribution

It presents a novel approach to logistic regression that accounts for data imprecision by considering a set of possible models derived from interval-valued data.

Findings

Enables inclusion of data uncertainties in logistic regression

Provides a framework for expressing epistemic uncertainty

Improves robustness of predictive models with imprecise data

Abstract

Logistic regression is an important statistical tool for assessing the probability of an outcome based upon some predictive variables. Standard methods can only deal with precisely known data, however many datasets have uncertainties which traditional methods either reduce to a single point or completely disregarded. In this paper we show that it is possible to include these uncertainties by considering an imprecise logistic regression model using the set of possible models that can be obtained from values from within the intervals. This has the advantage of clearly expressing the epistemic uncertainty removed by traditional methods.