Table 3 Accuracy of Random Forest classifier in predicting the prevalence of diseases in London areas. The results of two classifiers are reported: (i) binary classification of areas in the top and bottom quartiles of the three diseases’ prevalence; (ii) ternary classification where an equally-sized class containing training instances randomly sampled from the two central quartiles is added. The predictive features are six: gender, average age, education level, item weight, nutrient diversity, and calorie concentration. The accuracy of a random baseline classifier is 0.5 for the binary case, and 0.33 for the ternary case. Numbers in parenthesis represent the standard deviation on the 10-fold cross validation

From: Large-scale and high-resolution analysis of food purchases and health outcomes