lp alleviate this issue, choosing the model, adjusting the parameters and determining the evaluation criteria are of great importance. In this challenge, ensembles models exhibited more robust performance than individual models. In ensemble models, it is expected that the advantages of different methods complement each other and shortcomings are resolved when their predictions are integrated. Most of the teams trusted in the collaboration of multiple models instead of a single model. Boosting and voting classifiers have been the most preferred ensemble methods in the challenge. First and second-ranked teams utilized the Gradient Boosting (GB) classifier, after applying filtering-based feature selection. GB aims to learn from the misclassified samples in the previous step for improving the models to be able to classify them correctly. Thus, GB is prone to overfitting if it is not stopped early enough.

**Study limitations and recommendations for future work** A limitation of this challenge arose in the evaluation phase. Kaggle allows only a single test set and a scoring metric to measure the submission performance. Therefore, we randomly determined a portion of the dataset as test set and used accuracy as a scoring metric. We further circumvented the limitation of a single scoring metric by measuring the other two metrics, sensitivity and specificity on final submissions of predictions. A better evaluation scheme can be set up using cross-validation.

This study provided insights on the discriminative potential of cortical morphological networks in diagnosing autistic spectrum disorders using a large pool of machine learning techniques. However, for this Kaggle challenge, evaluation metrics were only restricted to classification accuracy, sensitivity and specificity. For translational medicine, clinicians can explore the designed algorithms using other evaluation metrics that they regard as important for their diagnosis. In this challenge, we have only utilized CMNs derived from maximum principle curvature of the 