Fig. 5: Overview of the model selection process.
From: Automated AI based identification of autism spectrum disorder from home videos
The dataset was split into a training-validation set (80%) and an independent hold-out test set (20%). Candidate models were trained separately for each video task (Name-response, imitation, and ball-playing) using stratified ten-fold cross-validation, and the model with the highest mean validation area under the receiver operating characteristic curve (AUROC) was selected. The selected models were then evaluated on the hold-out test set. ML machine learning.
