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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, 10:30-12:00Room: 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
- Analytics and Data Science
- Computer Science/Applications
- Machine Learning
Status: accepted
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