Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| F-beta rādītājs× | Makro vidējais F1× | |
|---|---|---|
| Nozare | Modeļu novērtēšana | Modeļu novērtēšana |
| Saime | MCDM | MCDM |
| Izcelsmes gads≠ | 1979 | 2000s |
| Autors≠ | C. J. van Rijsbergen | Multi-class evaluation community |
| Tips | Evaluation metric | Evaluation metric |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | F-measure with parameter beta | Macro F1, Unweighted average F1 |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
|
|