Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| F1-score× | Hamming-tab× | |
|---|---|---|
| Fagområde | Modelevaluering | Modelevaluering |
| Familie | MCDM | MCDM |
| Oprindelsesår≠ | 1979 | 2000s |
| Ophavsperson≠ | C. J. van Rijsbergen | Information theory and multi-label learning |
| Type≠ | Evaluation metric | Loss function |
| Oprindelig kilde≠ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ | Schapire, R. E., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3), 135-168. DOI ↗ |
| Aliasser | F-measure, Harmonic Mean | Hamming Distance, Subset Accuracy Loss |
| Relaterede≠ | 5 | 1 |
| Resumé≠ | 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. | Hamming loss measures the fraction of labels that are incorrectly predicted in multi-label classification. It counts the number of label mistakes divided by the total number of labels, providing a simple metric for multi-label problems. |
| ScholarGateDatasæt ↗ |
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