Anopheles number prediction on environmental and climate variables using Lasso and stratified two levels cross validation

Bienvenue Kouwaye (1, 2)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)
2 UAC - Université d’Abomey-Calavi = University of Abomey Calavi

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.

Mots clés: Lasso; variables selection; prediction; cross validation; Malaria

Domaines: Machine Learning [stat.ML]

Bienvenue Kouwaye. Anopheles number prediction on environmental and climate variables using Lasso and stratified two levels cross validation. 2016. ⟨hal-01351527⟩