Сравнение на методи

	Точност	Лог-загуба (Cross-Entropy Loss)
Област	Оценка на модели	Оценка на модели
Семейство	MCDM	MCDM
Година на възникване	20th century	1990s
Създател	Historical statistical foundations	Information theory and machine learning literature
Тип	Evaluation metric	Loss function
Основополагащ източник	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.
Други названия	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
Свързани	5	3
Резюме	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.