Aggregated ROC and PR curves for the directly imaged companions dataset. We used ROC and PR AUCs to quantify of the scoring quality of the ML methods relative to S/N. The ROC curves measure the trade-off between TPR and FPR; however, they tend to be over-optimistic in highly balanced frameworks, such as in imaging data. The P-R curves measure the trade-off between precision and recall.