Performances of the quantitative predictive models under six combinations of spatiotemporal resolutions.

The training and test observations were the data used for model training and test, respectively. The values in parentheses are the total number of zero values in each set. Max. R2 is the theoretical maximum value of R2 for a given number of obse...

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Bibliographic Details
Main Author:	Micanaldo Ernesto Francisco (18723706) (author)
Other Authors:	Thaddeus M. Carvajal (8796821) (author), Kozo Watanabe (544664) (author)
Published:	2024
Subjects:	Biochemistry Molecular Biology Biotechnology Ecology Cancer Infectious Diseases Plant Biology Virology Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines extreme gradient boosting conditional inference forest artificial neural networks impending dengue outbreaks spatiotemporal resolution differs fine spatiotemporal scales predicted dengue incidence dengue incidence may dengue incidence due generalized additive models dengue incidence prediction spatiotemporal dengue prediction quantitative pattern extraction six spatiotemporal resolutions six ml algorithms novel hybrid approach frequent zero values stage quantitative prediction dengue incidence data inflated models cannot enhance prediction accuracy qualitative dengue variables dengue incidence spatiotemporal resolutions hybrid approach quantitative prediction qualitative models prediction accuracy higher resolutions environmental variables binary variables qualitative predictions combining qualitative xlink "> study aimed rare phenomena random forests predicting zero model validation inflated data data distribution control strategies alternative solution
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Performances of the quantitative predictive models under six combinations of spatiotemporal resolutions.

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