Salīdzināt metodes

	Log-Loss (krustentropijas zudums)	F1-novērtējums
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	1990s	1979
Autors	Information theory and machine learning literature	C. J. van Rijsbergen
Tips	Loss function	Evaluation metric
Pirmavots	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Citi nosaukumi	Cross-Entropy Loss, Logloss	F-measure, Harmonic Mean
Saistītās	3	5
Kopsavilkums	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.