Treffer: Study Findings from State University of New York (SUNY) Buffalo Broaden Understanding of Machine Learning (Pygrf: an Improved Python Geographical Random Forest Model and Case Studies In Public Health and Natural Disasters).

A recent study conducted by researchers at the State University of New York (SUNY) Buffalo has focused on improving the accuracy and usability of the geographical random forest (GRF) machine learning model. The current GRF model has limitations in determining local model weight and bandwidth hyperparameters, as well as insufficient local training samples and high local prediction errors. To address these limitations, the researchers introduced theory-informed hyperparameter determination, expanded local training samples, and developed a Python-based GRF model called PyGRF. The study evaluated the performance of PyGRF on a dataset and demonstrated its use in two case studies related to public health and natural disasters. [Extracted from the article]

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