Anopheles number prediction on environmental and climate variables using Lasso and stratified two levels cross validation
Overview & Synopsis
This paper deals with prediction of anopheles number using environmental and climate variables. The variables selection is performed by an automatic machine learning method %don't get what you mean % %ok% based on Lasso and stratified two levels cross validation. Selected variables are debiased while the prediction is generated by simple GLM (Generalized linear model). Finally, the results reveal to be qualitatively better, at selection, the prediction, and the CPU time point of view than those obtained by B-GLM method.
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Document Information
| Document Title | Anopheles number prediction on environmental and climate variables using Lasso and stratified two levels cross validation |
|---|---|
| Author / Organization | Bienvenue Kouwaye |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2016 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-85099 |
| Original Source Link | Visit Source Page on hal.science » |