Answer / Gajram Singh

Support Vector Machines (SVM) is a supervised learning algorithm used for classification and regression. SVM finds the optimal hyperplane that best separates data points of different classes by maximizing the margin, which is half of the smallest distance between the hyperplane and the closest data points from each class (support vectors). If the data is not linearly separable, SVM can use kernel tricks like polynomial or radial basis function to transform the data into a higher dimensional space where it becomes linearly separable. For multi-class classification problems, SVM uses strategies such as one-vs-one and one-vs-all.

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