Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| F1 e peshuar× | F1 me mesatare mikronormale× | |
|---|---|---|
| Fusha | Vlerësimi i modeleve | Vlerësimi i modeleve |
| Familja | MCDM | MCDM |
| Viti i origjinës | 2000s | 2000s |
| Krijuesi | Multi-class evaluation community | Multi-class evaluation community |
| Lloji | Evaluation metric | Evaluation metric |
| Burimi themelues | 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 ↗ |
| Emërtime të tjera≠ | Support-weighted F1 | Micro F1, Frequency-weighted average F1 |
| Të lidhura≠ | 3 | 4 |
| Përmbledhja≠ | 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. | 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. |
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