Сравнение на методи

	Лог-загуба (Cross-Entropy Loss)	Бриер скор (Brier Score)
Област	Оценка на модели	Оценка на модели
Семейство	MCDM	MCDM
Година на възникване	1990s	1950
Създател	Information theory and machine learning literature	Glenn W. Brier
Тип	Loss function	Loss function
Основополагащ източник	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.
Други названия	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Свързани	3	3
Резюме	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.