Why Linear Models Can Still Work in a Complex and Changing World
Read the original on arXiv Machine Learning →The paper argues that linear models can still perform well in complex, changing environments because the world’s complexity often exceeds what can be reliably learned from finite, partially observed data. It explains that flexible, nonlinear models may capture idiosyncratic patterns specific to a particular setting—such as a hospital or policy period—leading to poor generalization when conditions shift. The authors formalize this intuition with a squared‑error proposition and discuss how factors like bias‑variance trade‑offs, regularization, measurement error, and distribution shift influence the choice between linear and nonlinear approaches.
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