方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| Log-Loss（交叉熵损失）× | 准确率× | F1分数× | |
|---|---|---|---|
| 领域 | 模型评估 | 模型评估 | 模型评估 |
| 方法族 | MCDM | MCDM | MCDM |
| 起源年份≠ | 1990s | 20th century | 1979 |
| 提出者≠ | Information theory and machine learning literature | Historical statistical foundations | C. J. van Rijsbergen |
| 类型≠ | Loss function | Evaluation metric | Evaluation metric |
| 开创性文献≠ | 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 ↗ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ |
| 别名 | Cross-Entropy Loss, Logloss | Overall Accuracy, Correct Classification Rate | F-measure, Harmonic Mean |
| 相关≠ | 3 | 5 | 5 |
| 摘要≠ | 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. | The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important. |
| ScholarGate数据集 ↗ |
|
|
|