Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| F-beta rādītājs× | F1 svērtais novērtējums× | |
|---|---|---|
| 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 | Support-weighted 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. | Weighted F1 computes the F1-score for each class and then takes a weighted average, where weights are proportional to the number of samples in each class (support). It provides a middle ground between macro and micro-averaging. |
| ScholarGateDatu kopa ↗ |
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