Why Linear Models Can Still Work in a Complex and Changing World
Linear models can beat nonlinear ones when unstable curvature fails to transfer across environments.
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Linear models can beat nonlinear ones when unstable curvature fails to transfer across environments.
Researchers introduce a unified probabilistic explainability framework using sparse anchored linear models that outperforms LIME and MAPLE on relevance error.
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