Machine Learning Project 12 — Using Support Vector Classification

Support Vector Machine — source pixabay.com

Today, let’s cover a new type of classification algorithm — “Support Vector Machine (SVM)”. The Math behind that is pretty complex so I will not be getting into that. I will try to explain the concept behind how the SVM classification algorithm works.

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I went through many tutorials to understand SVM — but the simplest explanation I found was from the A-Z Machine Learning course on Udemy. So I’ll be using the examples from that tutorial.

Understanding SVM

• Let’s look at the sample dataset below. We have some observations — some are red and some are green. We have already classified these points.

image source: A-Z Machine Learning Udemy

• We will use SVM to separates these 2 categories. Let us assume the line below is drawn by the SVM algorithm to separate the 2 categories and at the same time it has the maximum margin.
• By margin we mean, there will never be any data point inside the margin.
• This line is drawn equal distance from both the red and green points.