Salīdzināt metodes

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