改进地理随机森林模型，支持Python使用并提升预测精度。
PyGRF: An improved Python Geographical Random Forest model and case studies in public health and natural disasters
- 引入理论指导的超参数设定与局部样本扩展方法
- 在公共卫生与自然灾害案例中实现更精准预测
- 提供Python版本，方便机器学习从业者使用
地理随机森林（GRF）是一种新兴的空间显式机器学习模型，具备更准确的预测能力和局部解释性，已在多个研究中应用。然而，现有GRF在局部模型权重和带宽超参数确定、局部训练样本数量不足及局部预测误差偏高等方面存在局限。此外，当前实现为R语言包，缺乏Python版本，限制了其在偏好Python的机器学习从业者中的推广。本文通过引入理论指导的超参数设定、局部训练样本扩展以及空间加权的局部预测策略，改进了模型性能，并开发了基于Python的GRF模型与包PyGRF，以促进模型应用。我们在一个示例数据集上评估了PyGRF性能，并进一步展示了其在公共卫生与自然灾害两个案例研究中的实际应用价值。
原文摘要 · Abstract (English)
Geographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretations, GRF has already been used in many studies. The current GRF model, however, has limitations in its determination of the local model weight and bandwidth hyperparameters, potentially insufficient numbers of local training samples, and sometimes high local prediction errors. Also, implemented as an R package, GRF currently does not have a Python version which limits its adoption among machine learning practitioners who prefer Python. This work addresses these limitations by introducing theory-informed hyperparameter determination, local training sample expansion, and spatially-weighted local prediction. We also develop a Python-based GRF model and package, PyGRF, to facilitate the use of the model. We evaluate the performance of PyGRF on an example dataset and further demonstrate its use in two case studies in public health and natural disasters.
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