![[2602.19528] Beyond Accuracy: A Unified Random Matrix Theory Diagnostic Framework for Crash Classification Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.19528] Beyond Accuracy: A Unified Random Matrix Theory Diagnostic Framework for Crash Classification Models
Summary
This paper presents a novel diagnostic framework based on Random Matrix Theory for evaluating crash classification models, focusing on overfitting detection and model selection.
Why It Matters
The research addresses a critical gap in evaluating machine learning models for crash classification, moving beyond traditional metrics to provide deeper insights into model performance and reliability. This framework can enhance safety in transportation by ensuring better model validation.
Key Takeaways
- Introduces a spectral diagnostic framework for crash classification models.
- Identifies overfitting through power-law exponent analysis.
- Proposes an alpha-based early stopping criterion for model training.
- Demonstrates strong correlation between spectral quality and expert agreement.
- Framework is scalable for large datasets using Sparse Lanczos approximations.
Computer Science > Machine Learning arXiv:2602.19528 (cs) [Submitted on 23 Feb 2026] Title:Beyond Accuracy: A Unified Random Matrix Theory Diagnostic Framework for Crash Classification Models Authors:Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma View a PDF of the paper titled Beyond Accuracy: A Unified Random Matrix Theory Diagnostic Framework for Crash Classification Models, by Ibne Farabi Shihab and 2 other authors View PDF Abstract:Crash classification models in transportation safety are typically evaluated using accuracy, F1, or AUC, metrics that cannot reveal whether a model is silently overfitting. We introduce a spectral diagnostic framework grounded in Random Matrix Theory (RMT) and Heavy-Tailed Self-Regularization (HTSR) that spans the ML taxonomy: weight matrices for BERT/ALBERT/Qwen2.5, out-of-fold increment matrices for XGBoost/Random Forest, empirical Hessians for Logistic Regression, induced affinity matrices for Decision Trees, and Graph Laplacians for KNN. Evaluating nine model families on two Iowa DOT crash classification tasks (173,512 and 371,062 records respectively), we find that the power-law exponent $\alpha$ provides a structural quality signal: well-regularized models consistently yield $\alpha$ within $[2, 4]$ (mean $2.87 \pm 0.34$), while overfit variants show $\alpha < 2$ or spectral collapse. We observe a strong rank correlation between $\alpha$ and expert agreement (Spearman $\rho = 0.89$, $p < 0.001$), suggesting spectral quality captures mo...