Fig. 8.

Schematic representation for evaluation of the model with cross-validation. We use 5×10-fold cross-validation to evaluate each learner (the original EMPaSchiz-Learner, and its six variants). Here, we first divide the entire dataset of 174 subjects into 10 sets; we then use 9/10 of them to train the EMPaSchiz model (see Fig. 7b, c); we then run that model on the remaining 1/10 of the data (see Fig. 7a). We then compute accuracy as the number of correctly labelled instances, over all 10 folds, and use this as an estimate of the score of the learned EMPaSchiz -Performance system. We run this entire process five times—over five different partitionings and compute the overall accuracy of predictions over these 50 train-test splits. Trained models are depicted in green lines and predictions are depicted in red lines