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。
| Log-Loss（交差エントロピー損失）× | 精度× | |
|---|---|---|
| 分野 | モデル評価 | モデル評価 |
| 系統 | MCDM | MCDM |
| 提唱年≠ | 1990s | 20th century |
| 提唱者≠ | Information theory and machine learning literature | Historical statistical foundations |
| 種類≠ | Loss function | Evaluation metric |
| 原典≠ | Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. link ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| 別名 | Cross-Entropy Loss, Logloss | Overall Accuracy, Correct Classification Rate |
| 関連≠ | 3 | 5 |
| 概要≠ | Log-loss measures the difference between predicted probabilities and actual labels, penalizing confident wrong predictions more than uncertain ones. It is a standard loss function in machine learning optimization and evaluates probabilistic classifier calibration. | 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. |
| ScholarGateデータセット ↗ |
|
|