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arXiv · 2609.09547

A Statistical Approach to Estimating Sample Size of Machine Learning Models

Dat Phan-Trong·Sunil Gupta·Svetha Venkatesh
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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.

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Dat Phan-Trong, Sunil Gupta, Svetha Venkatesh. 2026-09-09. A Statistical Approach to Estimating Sample Size of Machine Learning Models. https://arxiv.org/abs/2609.09547

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