We used Principal Component Analysis (PCA) to visually assess the classification performance of RF and DNN according to each fingerprint, on the basis of the metrics obtained on the test set and averaged over all datasets (Tables ). To do so, for each classifier we first generated a matrix 𝐏 (20 ⨯ 6), where each row is a performance profile for a given fingerprint averaged over all datasets and each column is a different metric. We then added two rows to 𝐏, corresponding to two synthetic baselines: "best", corresponding to the best performance obtained in the benchmark for each metric, and "worst", corresponding to the worst performance for each figure of merit. Finally, for each classifier we used the first two principal components of P(22 ⨯ 6) to compute bidimensional embeddings of the performance profiles of all fingerprints (Figure and). Adding the "best" and "worst" rows to the matrix has the effect of stretching the variance towards the direction of performance variation, which is then captured by the PCA.