Learn, understand and implement one of the most powerful versatile Machine Learning Algorithms from first principles.
Support Vector Machines are very versatile Machine Learning algorithms. The main reason for their popularity is for their ability to perform both linear and non-linear classification and regression using what is known as the _kernel trick; _if you don’t know what that is, don’t worry. By the end of this article, you will be able to :
So, without further ado, let’s dive in!
With so many other algorithms out there(Linear Regression, Logistic Regression, Neural Networks, etc..) You may be wondering why you need to have another one in your toolkit! Perhaps these questions can be answered with the aid of a diagram:
Here we see three potential decision boundaries for classifying the data: H1, H2, and H3. First off, H1 does not separate the classes at all, so it is not a good hyperplane. H2 does separate the classes, however notice how the margin(or street) between the points is so small, and this classifier is very unlikely to perform well on unseen instances.
The third hyperplane, H3, represents the decision boundary of the SVM classifier; this line not only separates the two classes but also keeps the widest distance between the most extreme points of the two classes.
You can think of the SVM as fitting the widest possible margin between the two classes. This is known as large margin classification.
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