Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Exactitud× | Pèrdua logarítmica (Pèrdua d'entropia creuada)× | |
|---|---|---|
| Camp | Avaluació de models | Avaluació de models |
| Família | MCDM | MCDM |
| Any d'origen≠ | 20th century | 1990s |
| Autor original≠ | Historical statistical foundations | Information theory and machine learning literature |
| Tipus≠ | Evaluation metric | Loss function |
| Font seminal≠ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ | Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. link ↗ |
| Àlies | Overall Accuracy, Correct Classification Rate | Cross-Entropy Loss, Logloss |
| Relacionats≠ | 5 | 3 |
| Resum≠ | 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. | 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. |
| ScholarGateConjunt de dades ↗ |
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