RobustModelMaker: Coupling Bootstrap Stability Selection with Leakage-Safe Nested Cross-Validation for Scientific Machine Learning
arXiv:2606.01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures. Single-run feature selection produces feature sets that change substantially under small perturbations of the training data, and any procedure that uses the same data for selection, tuning, and evaluation produces optimistically biased performance estimates. The two failure modes are routinely treated as separable, but in the regimes where scientific data live, they interact: an unstable selection inflates the variance of an already-optimistic sc
The proliferation of machine learning in scientific research necessitates more robust and reliable methodologies to handle complex, often small-to-medium sized datasets.
Improving the reliability and reproducibility of scientific machine learning directly impacts the pace and integrity of discovery across various scientific and engineering disciplines.
This paper offers a new method to address the long-standing challenges of feature selection instability and optimistic performance estimation in scientific machine learning, potentially leading to more trustworthy AI applications in research.
- · Scientific researchers using AI
- · Machine learning ethics and safety organizations
- · AI model developers
- · Drug discovery and materials science
- · Researchers relying on less rigorous ML validation methods
- · AI solutions with unstable feature selection
Scientific fields will see an increase in the trustworthiness and reproducibility of machine learning-driven results.
This improved reliability could accelerate the adoption of AI in sensitive scientific applications, such as medical diagnostics or drug development.
Greater confidence in AI models may lead to broader scientific consensus and faster translation of research into real-world applications.
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Read at arXiv cs.LG