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Updated: Jul 14, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

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什么影响中国农民的相对贫困:基于统计和可解释的机器学习方法的全球分析

Wei Huang1, Yinke Liu1, Peiqi Hu1

  • 1School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.

Heliyon
|October 9, 2023
PubMed
概括

这项研究使用机器学习来确定影响中国农民相对贫困的关键因素. XGBoost实现了81.9%的准确性,为贫困治理和有针对性的干预提供了一个新的框架.

关键词:
确定因素的确定.机器学习 机器学习随机的森林随机的森林相对贫困 相对的贫困在XGBoost中使用.

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