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	Log-Loss (entropia krzyżowa)	Wynik Brier (Brier Score)	Wynik F1
Dziedzina	Ocena modeli	Ocena modeli	Ocena modeli
Rodzina	MCDM	MCDM	MCDM
Rok powstania	1990s	1950	1979
Twórca	Information theory and machine learning literature	Glenn W. Brier	C. J. van Rijsbergen
Typ	Loss function	Loss function	Evaluation metric
Źródło pierwotne	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.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Inne nazwy	Cross-Entropy Loss, Logloss	Mean Squared Probability Error	F-measure, Harmonic Mean
Pokrewne	3	3	5
Podsumowanie	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.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.