Сравнение на методи

	Бриер скор (Brier Score)	Лог-загуба (Cross-Entropy Loss)
Област	Оценка на модели	Оценка на модели
Семейство	MCDM	MCDM
Година на възникване	1950	1990s
Създател	Glenn W. Brier	Information theory and machine learning literature
Тип	Loss function	Loss function
Основополагащ източник	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Други названия	Mean Squared Probability Error	Cross-Entropy Loss, Logloss
Свързани	3	3
Резюме	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.	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.