aiqbench analysis
This paper addresses a critical bottleneck in deploying machine learning models by proposing a novel framework to estimate sample sizes for nonlinear algorithms without needing prior specification of effect structures. By approximating complex prediction surfaces with localized linear representations, the method enables more rigorous power analysis across diverse data regimes. This advancement is essential for researchers aiming to design efficient experiments and validate model robustness before large-scale deployment.
A Statistical Approach to Estimating Sample Size of Machine Learning Models
arXiv:2609.09547v1 Announce Type: cross Abstract: Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predictor-outcome relationship and effect structure to be specified a priori. Nonlinear ML models learn complex prediction surfaces that do not admit straightforward analytical power calculations. We propose a framework that approximates nonlinear ML models with localized linear representations and estimates sample size requirements by evaluating statistical power across these local regions.
Abstract:Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predictor-outcome relationship and effect structure to be specified a priori. Nonlinear ML models learn complex prediction surfaces that do not admit straightforward analytical power calculations. We propose a framework that approximates nonlinear ML models with localized linear representations and estimates sample size requirements by evaluating statistical power across these local regions.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.09547 [cs.LG]
(or arXiv:2609.09547v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.09547 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 166 words
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