<ul><li>Support Vectors: These are the points that are closest to the hyperplane. A separating line will be defined with the help of these data points.</li><li>Margin: It is the distance between the hyperplane and the observations closest to the hyperplane (support vectors). In SVM large margin is considered a good margin. There are two types of margins: hard margin and soft margin.</li><li>Hyperplanes are decision boundaries that help classify the data points. Data points falling on either side of the hyperplane can be attributed to different classes. Also, the dimension of the hyperplane depends upon the number of features.</li><li>If the number of input features is 2, then the hyperplane is just a line. If the number of input features is 3, then the hyperplane becomes a two-dimensional plane. It becomes difficult to imagine when the number of features exceeds 3.</li></ul>

Support Vector Machine (SVM) In SHORT

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