Salīdzināt metodes

	F1-novērtējums	Hamminga zudums
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	1979	2000s
Autors	C. J. van Rijsbergen	Information theory and multi-label learning
Tips	Evaluation metric	Loss function
Pirmavots	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	Schapire, R. E., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3), 135-168.
Citi nosaukumi	F-measure, Harmonic Mean	Hamming Distance, Subset Accuracy Loss
Saistītās	5	1
Kopsavilkums	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.