DAY 15-100 DAYS MLCODE: SVM Regression

This blog is a continuation of our previous blog about SVM, in today’s blog, we are going to focus on SVM Regression. We are going to apply the regression on the SVM model .

We’ll use the previous blog example and apply the regression on the previously created example.

SVM Regression

As per wiki SVM Regression :

In machine learning, support-vector machines (SVMs, also support-vector networks^[1]) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

https://en.wikipedia.org/wiki/Support-vector_machine

Below is one of the good example from the medium post ( Content copied from medium post of user savan patel).

Suppose you are given plot of two label classes on graph as shown in image (A). Can you decide a separating line for the classes?

You might have come up with something similar to following image (image B). It fairly separates the two classes. Any point that is left of line falls into black circle class and on right falls into blue square class. Separation of classes. That’s what SVM does. It finds out a line/ hyper-plane (in multidimensional space that separate outs classes). Shortly, we shall discuss why I wrote multidimensional space.