Support Vector Machine (SVM) is a supervised Machine Learning algorithm that performs classification tasks by constructing hyperplanes or classifiers in a multidimensional space that separates the data into two different classes. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables. In the diagram below you can see that there are two different classes of data( Blue and Red ) .You can separate them with several separating lines such as A, B, C and D. But there must be a single separating line or hyperplane that classifies the two classes best. So, the question is: How to choose the ultimate classifier? GOOD VS BAD CLASSIFIERS First it is necessary to know a few terms: Data that can be separated by a line or a simple hyperplane is known as linearly separable data. Now those hyperplanes that can linearly separate the data are known as linear classifiers. If the training data is linearly separable, we can obtain two parallel hyperplanes that separate the two classes of data, so that the distance between them is maximized. The region bounded by these two hyperplanes is called the “margin“, and the maximum margin hyperplane is the hyperplane that lies halfway between […]
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