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| Log-Loss (Entropia Incrociata)× | Accuratezza× | |
|---|---|---|
| Campo | Valutazione dei modelli | Valutazione dei modelli |
| Famiglia | MCDM | MCDM |
| Anno di origine≠ | 1990s | 20th century |
| Ideatore≠ | Information theory and machine learning literature | Historical statistical foundations |
| Tipo≠ | Loss function | Evaluation metric |
| Fonte seminale≠ | 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 ↗ |
| Alias | Cross-Entropy Loss, Logloss | Overall Accuracy, Correct Classification Rate |
| Correlati≠ | 3 | 5 |
| Sintesi≠ | 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. |
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