Extended Data Fig. 5: Confidence of model’s predictions.

The model robustness can be measured through the confidence of the predictions. The models that suffer from overfitting usually reach high performance on the training dataset by memorizing the specifics of the training data rather than learning the task at hand. As a consequence, such models result in incorrect but highly confident predictions during the deployment. The bar plots show the fraction of model predictions achieved with high confidence, for both correctly (blue) and incorrectly (yellow) estimated patient cases. The fraction of highly confident correctly predicted samples is consistently higher than the fraction of confident incorrect predictions across all the tasks. These results indicate the robustness of the model predictions for all tasks.