Support vector machines

A support vector machine (SVM) is a supervised machine learning model that works by identifying a hyperplane between represented data. The data can be represented in a multidimensional space. Thus, SVMs are widely used in classification models. In an SVM, the hyperplane that best separates the different classes will be used. In some cases, when we have different hyperplanes that separate different classes, identification of the correct one will be performed thanks to something called a margin, or a gap. The margin is the nearest distance between the hyperplanes and the data positions. You can take a look at the following representation to check for the margin:

The hyperplane with the highest gap will be selected. If we choose the hyperplane with the shortest margin, we might face misclassification problems later. Don't be distracted by the previous graph; the hyperplane will not always be linear. Consider a case like the following:

In the preceding situation, we can add a new axis, called the z axis, and apply a transformation using a kernel trick called a kernel function, where z=x^2+y^2. If you apply the transformation, the new graph will be as follows:

Now, we can identify the right hyperplane. The transformation is called a kernel. In the real world, finding a hyperplane is very hard. Thus, two important parameters, called regularization and gamma, play a huge role in the determination of the right hyperplane, and in every SVM classifier to obtain better accuracy in nonlinear hyperplane situations.