Figure 1:

a) An illustration of the importance-guided sequential model selection procedure used to find the optimal set of features. First, a full model including all features is trained using logistic regression with elastic net regularization to determine relative importance of individual features. Next, a series of truncated models were trained based on a progressively increasing set of top features rank ordered by the full model. The set of features in the best truncated model on the evaluation set were deemed as the optimal feature set. b) An illustration of the nested cross-validation procedure used to train, validate, and test the models. A grid search procedure with 3-fold cross-validation was implemented on the developmental set to determine the best model parameters. The resulting model was further tested on the evaluation set, which contained an independent set of participants not used in training and validation. The entire procedure was repeated on 100 different random partitioning of the data to allow for stable model performance.