Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Precīzijas un atsaukuma AUC× | F1-novērtējums× | |
|---|---|---|
| Nozare | Modeļu novērtēšana | Modeļu novērtēšana |
| Saime | MCDM | MCDM |
| Izcelsmes gads≠ | 2006 | 1979 |
| Autors≠ | Davis and Goadrich | C. J. van Rijsbergen |
| Tips | Evaluation metric | Evaluation metric |
| Pirmavots≠ | Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 233-240. DOI ↗ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ |
| Citi nosaukumi | PR AUC, PR Curve | F-measure, Harmonic Mean |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The Precision-Recall Area Under the Curve (PR AUC) is the area under the curve formed by plotting recall on the x-axis and precision on the y-axis. It is particularly useful for evaluating classifiers on imbalanced datasets, where it is often more informative than ROC AUC. | The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important. |
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