Uporedite metode

	Log-Loss (Gubitak po logaritmu / unakrsna entropija)	F1-mera
Oblast	Evaluacija modela	Evaluacija modela
Porodica	MCDM	MCDM
Godina nastanka	1990s	1979
Tvorac	Information theory and machine learning literature	C. J. van Rijsbergen
Tip	Loss function	Evaluation metric
Temeljni izvor	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Drugi nazivi	Cross-Entropy Loss, Logloss	F-measure, Harmonic Mean
Srodne	3	5
Sažetak	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 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.