ould result in overlooking actual autism subjects. High sensitivity can be favorable in population-based screening (level one) since it is desired to identify the maximum number of subjects at risk. Besides, specificity is more important in further evaluation (level two) to be applied to subjects that are categorized as at-risk previously [@lang2016early]. Because high specificity enables a model to discriminate the subjects not affected by ASD more accurately. Therefore, applying a classification method with high specificity followed by another with high specificity value can enhance the classification performance.

Bagging and boosting algorithms as ensemble methods have been successfully applied in high dimensional data classification problems [@pappu2014high]. Especially, boosting methods such as GradientBoosting and XGBoost showed their success in public challenges among many other methods [@chen2016xgboost]. In this challenge, the first two best performing teams (team-1 and team-2) exploited GB and outperformed other methodologies. Team-3 at 3rd rank used voting classifier in conjunction with different boosting methods such as AB, GB and XGB. The results clearly show that the ensemble methods can tackle the high dimensionality problem of CMNs better than individual traditional methods. The success of the ensemble methodology is rooted in the principle of combining weak classifiers as to complement each other’s weaknesses. The final combination model hopefully yields improved generalizability and robustness over a single model.

However, team-10 was the only team in the top 5 teams, which preferred DT as a single classifier (Table \[tab:methods\_details\]). Mostly, conventional classification models using high-dimensional data are more likely to overfit data than the ensemble methods [@pappu2014high]. Hence, for single models, the use of dimensionality reduction techniques such as feature selection or feature extraction are of greater importance than for ensemble methods. Team-10 applied PCA method after e