Support Vector Machines is one of the most famous supervised learning algorithms in Data Science. However, understanding how hyperparameters can affect the performance of this algorithm is quite tricky for a beginner.
When I had started learning SVM, I had found it difficult to imagine or picturize how the decision boundary and threshold lines change when I change the values of the hyperparameters or switch between kernels.
So, I decided to make a simple web app that helped students visualize the SVM algorithm according to their choice of hyperparameter setting.
The web app consists of two pages :
- The HOME page : The home page is basically the gateway to the visual tool. It also consists a link that redirects the user to blogs on SVM, in case the user wants to have a short read.
HOME PAGE SCREENSHOT :
- The main page : The SVM VISUAL TOOL
This page contains the main plot that is generated upon changing the settings below the plot on the left hand side.
THE PLOTS :
THE CONTROLS AND THE SHORT NOTES TO EXPLAIN THE HYPERPARAMETERS :
Tech stack : Frontend : HTML, CSS, Bootstrap, Flask.
Backend : Dash, Python.
VISIT THE WEB APP HERE : Welcome to the SVM Visual Tool(SVM VT)