Next, we used the remaining 315 features to train a random forest model to perform recursive feature elimination. In doing so, we removed features with importance lower than the 25th percentile of all feature importances, and used the remaining features to train a new model for the next iteration of feature elimination. This process was repeated for 15 iterations, after which we plotted the area under the receiver operating characteristic (ROC) curve for each iteration's model in ??; the area under the curve is denoted henceforth as "AUC" for brevity. The ROC curve is a plot of a binary classifier's true positive rate as a function of the false negative rate. AUC is commonlyused as an accuracy metric which ranges from 0 to 1, where 0.5 is random guessing and 1 is perfect accuracy. We select the feature set from the 11th iteration (16 features)