Machine learningSpatial machine learning
地理加权随机森林
地理加权随机森林（Geographically Weighted Random Forest, GWRF）是一种空间局部集成学习方法，它在每个观测位置拟合一个独立的随机森林模型，通过空间核函数对邻近的训练样本赋予比远处样本更重的权重。该方法由Stefanos Georganos及其同事于2019年提出（2021年发表），是对Breiman的随机森林的扩展，旨在处理空间非平稳性——即预测变量与响应变量之间的关系在地理空间上变化的现象。
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来源
- Georganos, S., et al. (2021). Geographical random forests: a spatial extension of the random forest algorithm. Geocarto International, 36(2), 121–136. link ↗
如何引用本页
ScholarGate. (2026, June 2). Geographically Weighted Random Forest (GWRF). ScholarGate. https://scholargate.app/zh/spatial-analysis/geographically-weighted-random-forest
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