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Table B.1.
Terms used when describing neural network performance from Pearson et al. (2019).
| Term | Definition | |
|---|---|---|
| Positive (P) | An object classified in the catalogues or identified by a network as a merger. | |
| Negative (N) | An object classified in the catalogues or identified by a network as a non-merger. | |
| True Positive (TP) | An object classified in the catalogues as a merger that is identified by a network as a merger. | |
| False Positive (FP) | An object classified in the catalogues as a non-merger that is identified by a network as a merger. | |
| True Negative (TN) | An object classified in the catalogues as a non-merger that is identified by a network as a non-merger. | |
| False Negative (FN) | An object classified in the catalogues as a merger that is identified by a network as a non-merger. | |
| Recall | Fraction of objects correctly identified by a network as a merger with respect to the total number of objects classified in the catalogues as mergers. | TP/(TP+FN) |
| Fall-out | Fraction of objects incorrectly identified by a network as a merger with respect to the total number of objects classified in the catalogues as mergers. | FP/(TP+FN) |
| Specificity | Fraction of objects correctly identified by a network as a non-merger with respect to the total number of objects classified in the catalogues as non-mergers. | TN/(TN+FP) |
| Precision | Fraction of objects correctly identified by a network as a merger with respect to the total number of objects identified by a network as a merger. | TP/(TP+FP) |
| Negative Predictive Value (NPV) | Fraction of objects correctly identified by a network as a non-merger with respect to the total number of objects identified by a network as a non-merger. | TN/(TN+FN) |
| Accuracy | Fraction of objects, both merger and non-merger, correctly identified by a network. | (TP+TN)/(TP+FP+TN+FN) |
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