Support vector machines belong to the family of supervised machine learning algorithms used for both classification and regression. Considering our binary classification problem, unlike logistic regression, the SVM algorithm will build a model around the training data in such a way that the training data points belonging to different classes are separated by a clear gap, which is optimized such that the distance of separation is the maximum. The samples on the margins are typically called the support vectors. The middle of the margin which separates the two classes is called the optimal separating hyperplane.
Data points on the wrong side of the margin are weighed down to reduce their influence and this is called the soft margin compared to the hard margins of separation we discussed earlier. SVM classifiers can be simple linear classifiers where the data points can be linearly separated. However, if we are dealing with data consisting of several features...
