Salīdzināt metodes

	Log-Loss (krustentropijas zudums)	Precizitāte	Briera rādītājs
Nozare	Modeļu novērtēšana	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM	MCDM
Izcelsmes gads	1990s	20th century	1950
Autors	Information theory and machine learning literature	Historical statistical foundations	Glenn W. Brier
Tips	Loss function	Evaluation metric	Loss function
Pirmavots	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.	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.
Citi nosaukumi	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate	Mean Squared Probability Error
Saistītās	3	5	3
Kopsavilkums	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.	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.