Сравнение методов

	Логарифмическая функция потерь (кросс-энтропия)	Оценка Бриера
Область	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM
Год появления	1990s	1950
Автор метода	Information theory and machine learning literature	Glenn W. Brier
Тип	Loss function	Loss function
Основополагающий источник	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.
Другие названия	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Связанные	3	3
Сводка	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.