Log Loss: Revision history

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18 March 2023

  • curprev 13:1213:12, 18 March 2023‎ Walle talk contribs‎ 2,368 bytes +2,368‎ Created page with "{{see also|Machine learning terms}} ==Log Loss== Log Loss, also known as logarithmic loss or cross-entropy loss, is a common loss function used in machine learning for classification problems. It is a measure of the difference between the predicted probabilities and the true labels of a dataset. The Log Loss function quantifies the performance of a classifier by penalizing the predicted probabilities that deviate from the actual class labels. ==Usage in Machine Learning..."
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