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| Log-Loss (entropia krzyżowa)× | Dokładność (Accuracy)× | |
|---|---|---|
| Dziedzina | Ocena modeli | Ocena modeli |
| Rodzina | MCDM | MCDM |
| Rok powstania≠ | 1990s | 20th century |
| Twórca≠ | Information theory and machine learning literature | Historical statistical foundations |
| Typ≠ | Loss function | Evaluation metric |
| Źródło pierwotne≠ | 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 ↗ |
| Inne nazwy | Cross-Entropy Loss, Logloss | Overall Accuracy, Correct Classification Rate |
| Pokrewne≠ | 3 | 5 |
| Podsumowanie≠ | 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. |
| ScholarGateZbiór danych ↗ |
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