Member-only story
Support Vector Machine: A Comprehensive Guide — Part2
In my last article, we discussed SVMs, the geometric intuition behind SVMs, and also Soft and Hard margins. Today we will continue discussing SVMs and will try to understand the mathematics behind SVMs, cost function, Support Vector Regressor (SVR), and Support Vector Kernels.

SVM Mathematical Intuition
In the image below, we have a best-fit line or a plane separating positive and negative lines. Also, there is a vector w perpendicular to the plane. Vector w and the positive points are on the same side of the plane so the distance between them is positive. On the other hand, negative points and vector w are on opposite sides so the distance between them is negative.

In the next image, we have a best-fit line or plane but along with that we also have two marginal planes passing through the nearest point of each category and the equation of the plane is also shown.
