A Statistical Approach to Estimating Sample Size of Machine Learning Models

2026-09-08

AI Summary

本文提出一种通过局部线性表示近似非线性机器学习模型来估计样本量的方法，以解决传统功效分析在复杂预测表面难以应用的问题。

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.

Problem

• sample size
• machine learning
• power analysis
• nonlinear models

Innovation

• Localized Linear Representations
• Statistical Power
• Sample Size Determination
• Machine Learning Models