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The goal of many machine learning algorithms, such as SVMs, is to find the optimal 'w' and 'b' that maximize the margin – the distance between the hyperplane and the closest data points from each class. A larger margin generally leads to better generalization performance on unseen data. The 'w' that achieves this is the best normal vector for separating your data.
