I wish to make a visualization of the separating hyperplane in the SVM algorithm along with my training features. As my feature vectors have 8 dimensions, we cannot directly visualize this. The only potential solution I can think of is to get a projection down to 2 dimensional space.

Will this solution even make sense? I know that SVMs will often make the data linearly separable in higher-dimensions and this may not occur in 2 dimensional space. Would this at least be a decent approximation?

How do people visualize their classification and their training features in practice? Hi

**Contents**hide

#### Best Answer

Here is a video I found that would be closest to what you are looking for: https://www.youtube.com/watch?v=3liCbRZPrZA

It shows SVM being applied to 3D. It is very difficult to graphically visualize dimensions higher than 3 and would usually cause more confusion.

With higher dimensions all I would use is vectors to and show significant points but this only would make sense if someone understands how it works in lower dimensions

### Similar Posts:

- Solved – svm classification visualization
- Solved – svm classification visualization
- Solved – Machine Learning : Classification algorithm for very high dimensional data which is uniquely definable in a very small sub-space
- Solved – How does linear SVMs function in multi dimensional feature space
- Solved – How does linear SVMs function in multi dimensional feature space