Fig. 2From: Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestusA Relative importance of XGBoost features that have the most influence in predicting the age classes of An. funestus. B Confusion matrix for predicting the age class of An. funestus using XGBoost on an unseen dataset; the results for the ML retrained with important features/wavenumbers (n = 100) identified by the initial XGBoost modelBack to article page