Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Mikro-gennemsnitlig F1× | Makro-gennemsnitlig F1× | |
|---|---|---|
| Fagområde | Modelevaluering | Modelevaluering |
| Familie | MCDM | MCDM |
| Oprindelsesår | 2000s | 2000s |
| Ophavsperson | Multi-class evaluation community | Multi-class evaluation community |
| Type | Evaluation metric | Evaluation metric |
| Oprindelig kilde | 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 ↗ |
| Aliasser | Micro F1, Frequency-weighted average F1 | Macro F1, Unweighted average F1 |
| Relaterede≠ | 4 | 3 |
| Resumé≠ | 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. | 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. |
| ScholarGateDatasæt ↗ |
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