Сравнение на методи

	Лог-загуба (Cross-Entropy Loss)	Точност
Област	Оценка на модели	Оценка на модели
Семейство	MCDM	MCDM
Година на възникване	1990s	20th century
Създател	Information theory and machine learning literature	Historical statistical foundations
Тип	Loss function	Evaluation metric
Основополагащ източник	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Други названия	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate
Свързани	3	5
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.	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.