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We here propose to use the area under the precision recall curve (AUPRC) as validation statistic.
Again, the area under the precision recall curve is more sensible than the area under the ROC curve.
As a drawback, the baseline of the area under the precision recall curve is equal to the ratio of active compounds in the dataset.
Figure 1 shows accuracy, the area under the ROC curve, enrichment factor, the area under the precision recall curve and the run-time for random forests.
Table 2 shows the area under the precision recall curve referred to in Figure 2, bounded at 25%, 50 % and 75 of recall.
Accordingly, the precision under the precision recall curve is neither "independent to extensive variables" [24] nor is its interpretation as intuitive as, e.g., AUROC scores.
Similar(35)
The area under the precision-recall curve (AUPRC, Fig. 3b) provides a robust metric for unbalanced datasets, which has been a critical measure in assessing clinical diagnostic tests24.
The area under the precision-recall curve was lower in BrIC (0.49) than just using horizontal rotational acceleration (0.8), which distinguished concussive and subconcussive motions better.
Rather than assessing prediction just for a single threshold, the performance over a range of cutoffs, including area under the ROC curve or area under the precision-recall curve metrics, is commonly reported.
Resource classification in categories existing in all repositories was automatically evaluated, obtaining maximum performance values of 84% (F1 score), 87.8% (area under the receiver operator characteristic curve), 86% (area under the precision-recall curve) and 75.1% (Cohen's κ).
Area under the precision-recall (PR) curve [28] was used as a metric in this work.
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