Figure 3 | Scientific Reports

Figure 3

From: Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa

Figure 3

MAE and MSE errors of used machine learning approaches on training DTRAS dataset. (a) Mean and Standard Deviation MAE. (b) Mean and Standard Deviation MSE. Algorithms in order from left to right per each regression task DT1M1–DT1M2: EN (filled circles); LASSO (filled squares); RR (filled up-triangles); LASSO-LARS (filled down-triangles); LASSO-LARS-AIC (empty circles); LASSO-LARS-BIC (empty squares); RF (empty up-triangles) and SVR (empty down-triangles). DTRAS Ibadan Dataset Training Set [from 1996 to 2014], EN elastic net, LASSO least absolute shrinkage and selection operator, RR ridge regression, LARS LEAST ANGLE REGRESSION, AIC Akaike information criterion, BIC Bayesian information criterion, SVR support vector regression, MAE mean absolute error, MSE mean square error.

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