方法对比
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| F-beta 分数× | 宏平均 F1× | |
|---|---|---|
| 领域 | 模型评估 | 模型评估 |
| 方法族 | MCDM | MCDM |
| 起源年份≠ | 1979 | 2000s |
| 提出者≠ | C. J. van Rijsbergen | Multi-class evaluation community |
| 类型 | Evaluation metric | Evaluation metric |
| 开创性文献≠ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. 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 ↗ |
| 别名≠ | F-measure with parameter beta | Macro F1, Unweighted average F1 |
| 相关≠ | 5 | 3 |
| 摘要≠ | The F-beta score is a weighted harmonic mean of precision and recall that allows customizing the relative importance of recall versus precision through a parameter beta. It generalizes the F1-score, which is the special case where beta = 1. | 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. |
| ScholarGate数据集 ↗ |
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