Training cross-validation results on adult TB samples.

Leave one out (LOOCV) areas under the receiver operating curves (AUCs) for models on the South Africa adult data. Each panel plots the AUC curve for six machine learning algorithms (glmnet: Elastic-Net logistic regression, knn: k-Nearest Neighbors, nnet: Neural Network, rf: Random Forest, svmRadial, Support Vector Machine with Radial Basis Function kernel; xgbTree: Extreme Gradient Boosting) starting with models trained using all 554 probes, and iteratively shrunk to models trained on 10 probes only. Models were trained to classify the data into 6 (TB:HIV+, TB:HIV-, LTB:HIV+, LTB:HIV-, OD:HIV+, OD:HIV-), 4 (TB:HIV+, TB:HIV-, LTB:HIV+, LTB:HIV-) and 2 (TB, LTB) classes. Two types of 2-class models were trained: using either HIV+ or HIV- samples. Error bars show bootstrap-estimated 95% confidence intervals around the AUC.