Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Log-verlies (Cross-Entropy Loss)× | Gemiddelde Absolute Fout (MAE)× | |
|---|---|---|
| Vakgebied | Modelevaluatie | Modelevaluatie |
| Familie | MCDM | MCDM |
| Jaar van ontstaan≠ | 1990s | 1799 |
| Grondlegger≠ | Information theory and machine learning literature | Pierre-Simon Laplace |
| Type≠ | Loss function | Robust distance-based metric |
| Oorspronkelijke bron≠ | Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. link ↗ | Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat. link ↗ |
| Aliassen≠ | Cross-Entropy Loss, Logloss | MAE, L1 error, mean absolute deviation |
| Verwant | 3 | 3 |
| Samenvatting≠ | 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. | Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values. |
| ScholarGateGegevensset ↗ |
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