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The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

Fig 1

Actual and predicted labels generate four outcomes of the confusion matrix.

(A) The left oval shows two actual labels: positives (P; blue; top half) and negatives (N; red; bottom half). The right oval shows two predicted labels: “predicted as positive” (light green; top left half) and “predicted as negative” (orange; bottom right half). A black line represents a classifier that separates the data into “predicted as positive” indicated by the upward arrow “P” and “predicted as negative” indicated by the downward arrow “N”. (B) Combining two actual and two predicted labels produces four outcomes: True positive (TP; green), False negative (FN; purple), False positive (FP; yellow), and True negative (TN; red). (C) Two ovals show examples of TPs, FPs, TNs, and FNs for balanced (left) and imbalanced (right) data. Both examples use 20 data instances including 10 positives and 10 negatives for the balanced, and 5 positives and 15 negatives for the imbalanced example.

Fig 1

doi: https://doi.org/10.1371/journal.pone.0118432.g001