手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 加重F1スコア× | マクロ平均F1× | |
|---|---|---|
| 分野 | モデル評価 | モデル評価 |
| 系統 | MCDM | MCDM |
| 提唱年 | 2000s | 2000s |
| 提唱者 | Multi-class evaluation community | Multi-class evaluation community |
| 種類 | Evaluation metric | Evaluation metric |
| 原典 | 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 ↗ |
| 別名≠ | Support-weighted F1 | Macro F1, Unweighted average F1 |
| 関連 | 3 | 3 |
| 概要≠ | 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. | 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データセット ↗ |
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