विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| लॉग-लॉस (क्रॉस-एंट्रॉपी लॉस)× | सटीकता× | |
|---|---|---|
| क्षेत्र | मॉडल मूल्यांकन | मॉडल मूल्यांकन |
| परिवार | MCDM | MCDM |
| उद्भव वर्ष≠ | 1990s | 20th century |
| प्रवर्तक≠ | Information theory and machine learning literature | Historical statistical foundations |
| प्रकार≠ | Loss function | Evaluation metric |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम | Cross-Entropy Loss, Logloss | Overall Accuracy, Correct Classification Rate |
| संबंधित≠ | 3 | 5 |
| सारांश≠ | 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. |
| ScholarGateडेटासेट ↗ |
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