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Can anyone explain the SVM in Machine Learning?

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Support vector machines (SVMs) are a type of supervised algorithm in Machine Learning. SVMs are often used for regression and classification-based problems. Its working involves the plotting of individual data points to each of the features present as a coordinate. Based on this, the classification operation is done to find out the plane in which there are differences between the vectors and the features that get mapped. The objective of SVM is simple; it is to find a hyperplane in the n-dimensional space, where 'n' refers to the number of features. Hyperplane itself is a decision boundary that is used to classify data points. So, all in all, these support vectors are close to the hyperplane, which correlates to the maximization of efficiency the classified can achieve.

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