Bandingkan metode

	Log-Loss (Cross-Entropy Loss)	Skor Brier
Bidang	Evaluasi Model	Evaluasi Model
Keluarga	MCDM	MCDM
Tahun asal	1990s	1950
Pencetus	Information theory and machine learning literature	Glenn W. Brier
Tipe	Loss function	Loss function
Sumber perintis	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.
Alias	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Terkait	3	3
Ringkasan	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.	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.