Machine Learning Project 13 — Using Kernel Support Vector Machine
In my last post, we covered the Support Vector Machine (SVM) classification algorithm and how it works. Today, we are going to talk about Kernel Support Vector Machine.
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The SVM works well in cases when data is linearly separable. Like in the diagram below, it is easy for us to find support vectors and separate the data like we did in Project 12.
But what if our dataset contains points as shown below. We cannot draw a line to separate the data. No matter which way we draw the line, we cannot separate these points.
So in above case, we cannot separate the points using standard SVM. This is because the above data is not linearly separable. The SVM algorithm by default makes the assumption that the data is linearly separable. So if our data is not linearly separable like above, the SVM will not work.
