Vertaile menetelmiä

	Log-Loss (ristientropiahäviö)	F1-pisteet
Tieteenala	Mallien arviointi	Mallien arviointi
Menetelmäperhe	MCDM	MCDM
Syntyvuosi	1990s	1979
Kehittäjä	Information theory and machine learning literature	C. J. van Rijsbergen
Tyyppi	Loss function	Evaluation metric
Alkuperäislähde	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Rinnakkaisnimet	Cross-Entropy Loss, Logloss	F-measure, Harmonic Mean
Liittyvät	3	5
Tiivistelmä	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.