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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.13947 |
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| _version_ | 1866913513381822464 |
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| author | Sun, Kai Zhou, Ryan Zhenqi Kim, Jiyeon Hu, Yingjie |
| author_facet | Sun, Kai Zhou, Ryan Zhenqi Kim, Jiyeon Hu, Yingjie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13947 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyGRF: An improved Python Geographical Random Forest model and case studies in public health and natural disasters Sun, Kai Zhou, Ryan Zhenqi Kim, Jiyeon Hu, Yingjie Computers and Society Machine Learning 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. |
| title | PyGRF: An improved Python Geographical Random Forest model and case studies in public health and natural disasters |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2409.13947 |