Hinge loss: Revision history

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20 March 2023

  • curprev 05:0405:04, 20 March 2023‎ Walle talk contribs‎ 2,980 bytes +2,980‎ Created page with "{{see also|Machine learning terms}} ==Hinge Loss== Hinge loss is a type of loss function used in machine learning and specifically in support vector machines (SVMs). It measures the error between the predicted output and the actual output for a given training example. Hinge loss is particularly effective for binary classification problems, as it aims to find the optimal decision boundary (or margin) that maximally separates two classes of data points. ===Definit..."
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