Compara mètodes

	Pèrdua de Hamming	Índex de Jaccard
Camp	Avaluació de models	Avaluació de models
Família	MCDM	MCDM
Any d'origen	2000s	1901
Autor original	Information theory and multi-label learning	Paul Jaccard
Tipus	Loss function	Similarity metric
Font seminal	Schapire, R. E., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3), 135-168.	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.
Àlies	Hamming Distance, Subset Accuracy Loss	Jaccard Similarity, Intersection over Union (IoU)
Relacionats	1	2
Resum	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.