Kernel trick

We have already seen that SVM works smoothly when it comes to having linear separable data. Just have a look at the following figure; it depicts that vectors are not linearly separable, but the noticeable part is that it is not being separable in 2D space:

With a few adjustments, we can still make use of SVM here.

Transformation of a two-dimensional vector into a 3D vector or any other higher dimensional vector can set things right for us. The next step would be to train the SVM using a higher dimensional vector. But the question arises of how high in dimension we should go to transform the vector. What this means is if the transformation has to be a two-dimensional vector, or 3D or 4D or more. It actually depends on the which brings separability into the dataset.

Kernel

A non-separable dataset like the one used previously is always a tough thing to deal with, however, there are ways to deal with it. One way is to set the vectors into higher dimensions through transformation. But...