Linganisha mbinu

	Alama ya Brier	Usahihi	Log-Loss (aucdoti ya msalaba-entropi)
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM	MCDM
Mwaka wa asili	1950	20th century	1990s
Mwanzilishi	Glenn W. Brier	Historical statistical foundations	Information theory and machine learning literature
Aina	Loss function	Evaluation metric	Loss function
Chanzo asilia	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.
Majina mbadala	Mean Squared Probability Error	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
Zinazohusiana	3	5	3
Muhtasari	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.