Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Pérdida Logarítmica (Entropía Cruzada)× | Exactitud× | |
|---|---|---|
| Campo | Evaluación de modelos | Evaluación de modelos |
| Familia | MCDM | MCDM |
| Año de origen≠ | 1990s | 20th century |
| Autor original≠ | Information theory and machine learning literature | Historical statistical foundations |
| Tipo≠ | Loss function | Evaluation metric |
| Fuente seminal≠ | Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. link ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Alias | Cross-Entropy Loss, Logloss | Overall Accuracy, Correct Classification Rate |
| Relacionados≠ | 3 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
|
|