Web Reference: Aug 1, 2025 · The cross-entropy loss is a scalar value that quantifies how far off the model's predictions are from the true labels. For each sample in the dataset, the cross-entropy loss reflects how well the model's prediction matches the true label. The cross entropy arises in classification problems when introducing a logarithm in the guise of the log-likelihood function. This section concerns the estimation of the probabilities of different discrete outcomes. Feb 27, 2026 · Cross-entropy is a popular loss function used in machine learning to measure the performance of a classification model. Namely, it measures the difference between the discovered probability distribution of a classification model and the predicted values.
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Last Updated: April 9, 2026
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