Fig. 2

ROC curves showing the performance of different machine learning algorithms. The performance of 8 machine learning algorithms is illustrated by ROC curves. The abscissa axis corresponds to the false-positive rate, i.e., 1.0 − specificity. The ordinate axis corresponds to the true positive rate, i.e., the recall or sensitivity. Different thresholds for the values reported by an algorithm represent different points along the algorithm’s curve in the figure. The dotted line with unit slope indicates the performance of a naïve random algorithm. For each algorithm, the area under the curve (AUC) is indicated