The limits of interpretability in multiple linear regression
A new paper published on arXiv explores the limitations of interpretability in multiple linear regression models, particularly when dealing with multicollinearity. The research theoretically analyzes how correlated input features can lead to unstable and oscillatory weights, hindering physical interpretation. While Ridge regularization can suppress these unstable modes, the paper emphasizes that caution is still needed when interpreting the resulting weights, even in these simpler models compared to deep neural networks. AI
IMPACT Highlights challenges in interpreting even simple linear models, suggesting caution is needed when drawing conclusions from AI outputs.