Linganisha mbinu

	Log-Loss (aucdoti ya msalaba-entropi)	Alama ya Brier
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM
Mwaka wa asili	1990s	1950
Mwanzilishi	Information theory and machine learning literature	Glenn W. Brier
Aina	Loss function	Loss function
Chanzo asilia	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.
Majina mbadala	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Zinazohusiana	3	3
Muhtasari	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.