Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Mikro-gennemsnitlig F1× | Nøjagtighed× | |
|---|---|---|
| Fagområde | Modelevaluering | Modelevaluering |
| Familie | MCDM | MCDM |
| Oprindelsesår≠ | 2000s | 20th century |
| Ophavsperson≠ | Multi-class evaluation community | Historical statistical foundations |
| 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 ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Aliasser | Micro F1, Frequency-weighted average F1 | Overall Accuracy, Correct Classification Rate |
| Relaterede≠ | 4 | 5 |
| 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. | Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class. |
| ScholarGateDatasæt ↗ |
|
|