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. Dec 22, 2020 · Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions.
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