This procedure provides an intuitive way to assess classification performance: the more a fingerprint is similar to the "best" baseline in the scores plot, the better it performed across all metrics, and vice-versa for the "worst" baseline. Deviation from the "best-worst" direction capture the variability in the performance based on the considered metric. Additionally, inspection of the principal component loadings allows to understand which aspects of the classification performance they model: while the first component gives large weights to most metrics, the second component focuses mostly on capturing the tradeoff between precision and recall in imbalanced classification (Figure ). Therefore, high precision The closer a fingerprint is to the "best" synthetic baseline, the better it approximates the best possible performance according to all evaluated classification metrics. The black dotted line connecting "worst"