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	Log-Loss (entropia krzyżowa)	Dokładność (Accuracy)	Wynik F1
Dziedzina	Ocena modeli	Ocena modeli	Ocena modeli
Rodzina	MCDM	MCDM	MCDM
Rok powstania	1990s	20th century	1979
Twórca	Information theory and machine learning literature	Historical statistical foundations	C. J. van Rijsbergen
Typ	Loss function	Evaluation metric	Evaluation metric
Źródło pierwotne	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.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Inne nazwy	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean
Pokrewne	3	5	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.	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 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.