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| Log-Loss（交差エントロピー損失）× | ブライアースコア× | |
|---|---|---|
| 分野 | モデル評価 | モデル評価 |
| 系統 | MCDM | MCDM |
| 提唱年≠ | 1990s | 1950 |
| 提唱者≠ | Information theory and machine learning literature | Glenn W. Brier |
| 種類 | Loss function | Loss function |
| 原典≠ | Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. link ↗ | Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3. DOI ↗ |
| 別名≠ | Cross-Entropy Loss, Logloss | Mean Squared Probability Error |
| 関連 | 3 | 3 |
| 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. | The Brier score measures the mean squared difference between predicted probabilities and actual binary outcomes. It is a simple, interpretable metric for evaluating the accuracy of probabilistic predictions, particularly in weather forecasting and medical diagnosis. |
| ScholarGateデータセット ↗ |
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