<ul><li>Support Vector Machines (SVMs) use hinge loss to confidently separate data points into distinct classes.</li><li>SVMs aim to find the best line with the widest margin between classes, even allowing for some errors with soft margins.</li><li>Hinge loss measures the model's confidence in its decisions, encouraging a clear margin of difference between classes.</li><li>While logistic regression uses log loss for smooth and probabilistic decisions, SVMs use hinge loss for sharp decision boundaries.</li></ul>

Hinge Loss: Understanding and Implementing it from Scratch

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