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。
| ハミング損失× | ジャカード指数× | |
|---|---|---|
| 分野 | モデル評価 | モデル評価 |
| 系統 | MCDM | MCDM |
| 提唱年≠ | 2000s | 1901 |
| 提唱者≠ | Information theory and multi-label learning | Paul Jaccard |
| 種類≠ | Loss function | Similarity metric |
| 原典≠ | Schapire, R. E., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3), 135-168. DOI ↗ | Jaccard, P. (1901). Etude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, 37, 547-579. link ↗ |
| 別名 | Hamming Distance, Subset Accuracy Loss | Jaccard Similarity, Intersection over Union (IoU) |
| 関連≠ | 1 | 2 |
| 概要≠ | Hamming loss measures the fraction of labels that are incorrectly predicted in multi-label classification. It counts the number of label mistakes divided by the total number of labels, providing a simple metric for multi-label problems. | The Jaccard index measures the similarity between predicted and true label sets by computing the ratio of intersection to union. It is widely used in multi-label classification and set-based similarity tasks where partial overlap is important. |
| ScholarGateデータセット ↗ |
|
|