विधियों की तुलना करें

	सटीकता	लॉग-लॉस (क्रॉस-एंट्रॉपी लॉस)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	20th century	1990s
प्रवर्तक	Historical statistical foundations	Information theory and machine learning literature
प्रकार	Evaluation metric	Loss function
मौलिक स्रोत	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.
उपनाम	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
संबंधित	5	3
सारांश	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.