Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models

Kentaro Hoffman; Stephen Salerno; Jeff Leek; Tyler McCormick

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arXiv · 2405.13926

Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models

Kentaro Hoffman·Stephen Salerno·Jeff Leek·Tyler McCormick

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Abstract

Large-scale prediction models using tools from artificial intelligence (AI) or machine learning (ML) are increasingly common across a variety of industries and scientific domains. Despite their effectiveness, training AI and ML tools at scale can cost tens or hundreds of thousands of dollars (or more); and even after a model is trained, substantial resources must be invested to keep models up-to-date. This paper presents a decision-theoretic framework for deciding when to refit an AI/ML model when the goal is to perform unbiased statistical inference using partially AI/ML-generated data. Drawing on portfolio optimization theory, we treat the decision of {\it recalibrating} a model or statistical inference versus {\it refitting} the model as a choice between ``investing'' in one of two ``assets.'' One asset, recalibrating the model based on another model, is quick and relatively inexpensive but bears uncertainty from sampling and may not be robust to model drift. The other asset, {\it refitting} the model, is costly but removes the drift concern (though not statistical uncertainty from sampling). We present a framework for balancing these two potential investments while preserving statistical validity. We evaluate the framework using simulation and data on electricity usage and predicting flu trends.

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Kentaro Hoffman, Stephen Salerno, Jeff Leek, Tyler McCormick. 2024-05-22. Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models. https://arxiv.org/abs/2405.13926

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