Statistical Learning: Foundations, Bias-Variance and the Art of Estimation
Abstract Imagine you are a hospital administrator deciding whether to deploy a machine learning model that predicts which ICU patients will deteriorate in the next six hours. The model was built by a talented team, trained on two years of electronic health records, and achieves 89% accuracy on a held-out test set. The question you actually need to answer is not in the model card: what does the model not know — and what can it not know, regardless of how much more data you feed it? ...