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Updated: Sep 13, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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在预测模型中纳入纵向可变性:在长期随访的队列研究中对机器学习和后勤回归进行比较

L M de Groot1, J W R Twisk1, A A L Kok2

  • 1Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, the Netherlands; Amsterdam Public Health, Methodology Program, Amsterdam, the Netherlands.

Annals of epidemiology
|July 28, 2025
PubMed
概括

添加纵向数据的可变性可以改善预测模型. 这种增强的方法对老年人群特别有用,可以提高模型性能,而不仅仅是考虑平均值和随时间变化.

关键词:
拉索回归是一种回归方式.纵向预测器是一个长度预测器.机器学习 机器学习预测 预测 预测随机的森林 随机的森林可变性的变化.

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