Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| F1 pesato× | F1 macro-medio× | |
|---|---|---|
| Campo | Valutazione dei modelli | Valutazione dei modelli |
| Famiglia | MCDM | MCDM |
| Anno di origine | 2000s | 2000s |
| Ideatore | Multi-class evaluation community | Multi-class evaluation community |
| Tipo | Evaluation metric | Evaluation metric |
| Fonte seminale | 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 ↗ |
| Alias≠ | Support-weighted F1 | Macro F1, Unweighted average F1 |
| Correlati | 3 | 3 |
| Sintesi≠ | 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. | 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. |
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