Сравнение методов

	Точность	Логарифмическая функция потерь (кросс-энтропия)
Область	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM
Год появления	20th century	1990s
Автор метода	Historical statistical foundations	Information theory and machine learning literature
Тип	Evaluation metric	Loss function
Основополагающий источник	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.
Другие названия	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
Связанные	5	3
Сводка	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.