Recent studies are increasingly using Machine Learning (ML) approaches to predict and/or classify various neuropsychiatric disorders and outcomes [14,15,16], including AUD [11,17,18]. ML is becoming an essential part of data analytics [16], which can also handle numerous variables on a smaller sample size [19]. Random Forest (RF) is a widely used ML method to predict/classify individuals with a particular diagnosis from the unaffected controls [20]. RF uses randomly generated bootstrapped data sets that can then be used to train an ensemble of decision trees, which will determine an outcome by a majority “vote” to classify the data [21]. The main advantages of RF methods are: (i) they are non-parametric and therefore do not depend on the distribution of the data [20], they relatively have a smaller bias and less variance resulting in good generalization power [22], and (iii) they gracefully handle multi-collinearity in data, a problem that destabilizes traditional regression-based methods.
