A Statistical Approach to Estimating Sample Size of Machine Learning Models
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.
| Authors | Dat Phan-Trong, Sunil Gupta, Svetha Venkatesh |
|---|---|
| Primary category | cs.LG |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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