Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| F1 mediată la nivel micro× | 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≠ | Micro F1, Frequency-weighted average F1 | Support-weighted F1 |
| Înrudite≠ | 4 | 3 |
| Rezumat≠ | Micro-averaged F1 computes the F1-score by aggregating true positives, false positives, and false negatives across all classes, then calculating a single metric. It is equivalent to accuracy in multi-class classification and is useful when class distributions reflect their natural importance. | 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 ↗ |
|
|