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| Log-Loss (entropia krzyżowa)× | Wynik Brier (Brier Score)× | |
|---|---|---|
| Dziedzina | Ocena modeli | Ocena modeli |
| Rodzina | MCDM | MCDM |
| Rok powstania≠ | 1990s | 1950 |
| Twórca≠ | Information theory and machine learning literature | Glenn W. Brier |
| Typ | Loss function | Loss function |
| Źródło pierwotne≠ | Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. link ↗ | Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3. DOI ↗ |
| Inne nazwy≠ | Cross-Entropy Loss, Logloss | Mean Squared Probability Error |
| Pokrewne | 3 | 3 |
| 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. | The Brier score measures the mean squared difference between predicted probabilities and actual binary outcomes. It is a simple, interpretable metric for evaluating the accuracy of probabilistic predictions, particularly in weather forecasting and medical diagnosis. |
| ScholarGateZbiór danych ↗ |
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