Confronta i metodi

	Accuratezza	Log-Loss (Entropia Incrociata)	Errore Assoluto Medio (MAE)
Campo	Valutazione dei modelli	Valutazione dei modelli	Valutazione dei modelli
Famiglia	MCDM	MCDM	MCDM
Anno di origine	20th century	1990s	1799
Ideatore	Historical statistical foundations	Information theory and machine learning literature	Pierre-Simon Laplace
Tipo	Evaluation metric	Loss function	Robust distance-based metric
Fonte seminale	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
Alias	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss	MAE, L1 error, mean absolute deviation
Correlati	5	3	3
Sintesi	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.	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.