Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| F1-macro× | F1-score promediado por microclase× | |
|---|---|---|
| Campo | Evaluación de modelos | Evaluación de modelos |
| Familia | MCDM | MCDM |
| Año de origen | 2000s | 2000s |
| Autor original | Multi-class evaluation community | Multi-class evaluation community |
| Tipo | Evaluation metric | Evaluation metric |
| Fuente seminal | 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 | Macro F1, Unweighted average F1 | Micro F1, Frequency-weighted average F1 |
| Relacionados≠ | 3 | 4 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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