Scientists propose machine learning approach to solve the world’s food insecurity issue
Machine learning can guide food security efforts when primary data are not available. Predictions that differ from the observed value by maximum ± 5 prevalence points are classified as correct. Predicted prevalence >40% (40%) are classified as high over-estimation (under-estimation). The other regions are classified as low under- and over-estimation. The solid black line indicates where the points would fall if all predicted values perfectly…