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Paper reveals interpretability limits in linear regression models

By PulseAugur Editorial · [2 sources] ·

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.

RANK_REASON The cluster contains a pre-print academic paper discussing theoretical and numerical analysis of machine learning models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Anand Sharma, Chen Liu, Daniele Coslovich, Misaki Ozawa ·

    The limits of interpretability in multiple linear regression

    arXiv:2606.16013v1 Announce Type: cross Abstract: Interpreting machine-learning models has attracted increasing attention, particularly in the physical sciences, where one often seeks to understand the underlying mechanisms rather than merely make predictions. Multiple linear reg…

  2. arXiv stat.ML TIER_1 English(EN) · Misaki Ozawa ·

    The limits of interpretability in multiple linear regression

    Interpreting machine-learning models has attracted increasing attention, particularly in the physical sciences, where one often seeks to understand the underlying mechanisms rather than merely make predictions. Multiple linear regression is often regarded as an interpretable alte…

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