New paper questions cross-validation stability for model comparison

A new paper published on arXiv demonstrates that cross-validation, a common statistical technique for comparing machine learning models, can produce unstable and invalid inferences. The research specifically highlights that the Lasso and soft-thresholding methods, despite being individually stable, can lead to unreliable comparisons. This instability calls into question the routine use of cross-validation for model comparison without prior verification of relative stability. AI

IMPACT Highlights potential flaws in standard model evaluation techniques, urging caution in interpreting comparative results.

RANK_REASON The cluster contains an academic paper detailing a new research finding about statistical methods in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• Alexandre Bayle
• arXiv
• Cross-validation
• Lasso

• paper
• other

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