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| 로그 손실(교차 엔트로피 손실)× | 정확도× | |
|---|---|---|
| 분야 | 모델 평가 | 모델 평가 |
| 계열 | MCDM | MCDM |
| 기원 연도≠ | 1990s | 20th century |
| 창시자≠ | Information theory and machine learning literature | Historical statistical foundations |
| 유형≠ | Loss function | Evaluation metric |
| 원전≠ | 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 ↗ |
| 별칭 | Cross-Entropy Loss, Logloss | Overall Accuracy, Correct Classification Rate |
| 관련≠ | 3 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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