Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Hamming Loss× | Jaccardův index× | |
|---|---|---|
| Obor | Hodnocení modelů | Hodnocení modelů |
| Rodina | MCDM | MCDM |
| Rok vzniku≠ | 2000s | 1901 |
| Tvůrce≠ | Information theory and multi-label learning | Paul Jaccard |
| Typ≠ | Loss function | Similarity metric |
| Původní zdroj≠ | Schapire, R. E., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3), 135-168. DOI ↗ | Jaccard, P. (1901). Etude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, 37, 547-579. link ↗ |
| Další názvy | Hamming Distance, Subset Accuracy Loss | Jaccard Similarity, Intersection over Union (IoU) |
| Příbuzné≠ | 1 | 2 |
| Shrnutí≠ | 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. | The Jaccard index measures the similarity between predicted and true label sets by computing the ratio of intersection to union. It is widely used in multi-label classification and set-based similarity tasks where partial overlap is important. |
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