Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
arxiv(2024)
摘要
Retraining machine learning models remains an important task for real-world
machine learning model deployment. Existing methods focus largely on greedy
approaches to find the best-performing model without considering the stability
of trained model structures across different retraining evolutions. In this
study, we develop a mixed integer optimization algorithm that holistically
considers the problem of retraining machine learning models across different
data batch updates. Our method focuses on retaining consistent analytical
insights - which is important to model interpretability, ease of
implementation, and fostering trust with users - by using custom-defined
distance metrics that can be directly incorporated into the optimization
problem. Importantly, our method shows stronger stability than greedily trained
models with a small, controllable sacrifice in model performance in a
real-world production case study. Finally, important analytical insights, as
demonstrated using SHAP feature importance, are shown to be consistent across
retraining iterations.
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