# A Statistical Approach to Estimating Sample Size of Machine Learning Models

arXiv: https://arxiv.org/abs/2609.09547 · HTML: https://mlsanity.com/paper/2609.09547/

## Key facts

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- First posted on arXiv 9 Sep 2026.

## Abstract (arXiv)

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.