Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| F1 mediu-macro× | F1 ponderat× | |
|---|---|---|
| Domeniu | Evaluarea modelelor | Evaluarea modelelor |
| Familie | MCDM | MCDM |
| Anul apariției | 2000s | 2000s |
| Autorul original | Multi-class evaluation community | Multi-class evaluation community |
| Tip | Evaluation metric | Evaluation metric |
| Sursa seminală | 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 ↗ | 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 ↗ |
| Denumiri alternative≠ | Macro F1, Unweighted average F1 | Support-weighted F1 |
| Înrudite | 3 | 3 |
| Rezumat≠ | 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. | 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. |
| ScholarGateSet de date ↗ |
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