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Spark for Data Science by Bikramaditya Singhal, Srinivas Duvvuri

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Linear Support Vector Machines (SVM)

Support Vector Machines (SVM) is a type of supervised machine learning algorithm and can be used for both classification and regression. However, it is more popular in addressing the classification problems, and since Spark offers it as an SVM classifier, we will limit our discussion to the classification setting only. When used as a classifier, unlike logistic regression, it is a non-probabilistic classifier.

The SVM has evolved from a simple classifier called the maximal margin classifier. Since the maximal margin classifier required that the classes be separable by a linear boundary, it could not be applied to many datasets. So it was extended to an improved version called the support vector classifier

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