The Use of Support Vector Machine to Analyze Food Security in a Region of Brazil

The application of Support Vector Machine (SVM) to classify food security in a northeast region in Brazil is explored. This type of application represents a novel use of the SVM in addressing contemporary social science questions. The results demonstrate an accuracy > 75% and a recall of 84% for classifying households that are food insecure. The variables identified by the model are consistent with contemporary theories of food security and vulnerability. The successful application of SVM in this instance and the growing availability of large-scale social science datasets suggest that data mining techniques will have a larger role to play in answering critical social science questions in the future.