EURO 2024 Copenhagen
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323. Estimated economic impact of model drift

Invited abstract in session MB-28: Advancements of OR-analytics in statistics, machine learning and data science 2, stream Advancements of OR-analytics in statistics, machine learning and data science.

Monday, 10:30-12:00
Room: 065 (building: 208)

Authors (first author is the speaker)

1. Andrew Clark

Abstract

In the dynamic landscape of data-driven business operations, model drift poses a significant challenge, often leading to substantial economic repercussions. Traditional approaches to quantifying the impact of model drift have been rudimentary, frequently relying on simplistic, ad-hoc calculations. Our paper introduces the Economic Impact Estimation Metric, a pioneering method designed to assign a quantifiable fiat cost to the concept of model drift in a systematic and accessible manner.

Keywords

Status: accepted


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