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| Puntuació F-beta× | F1-score mitjà macro× | |
|---|---|---|
| Camp | Avaluació de models | Avaluació de models |
| Família | MCDM | MCDM |
| Any d'origen≠ | 1979 | 2000s |
| Autor original≠ | C. J. van Rijsbergen | Multi-class evaluation community |
| Tipus | Evaluation metric | Evaluation metric |
| Font seminal≠ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ | Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63. link ↗ |
| Àlies≠ | F-measure with parameter beta | Macro F1, Unweighted average F1 |
| Relacionats≠ | 5 | 3 |
| Resum≠ | The F-beta score is a weighted harmonic mean of precision and recall that allows customizing the relative importance of recall versus precision through a parameter beta. It generalizes the F1-score, which is the special case where beta = 1. | Macro-averaged F1 computes the F1-score independently for each class and then takes the unweighted arithmetic mean. It treats all classes equally, regardless of their frequency in the dataset, making it useful for imbalanced multi-class problems. |
| ScholarGateConjunt de dades ↗ |
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