Method of Lagrange Multipliers: The Theory Behind Support Vector Machines (Part 3: Implementing An SVM From Scratch In Python)
The mathematics that powers a support vector machine (SVM) classifier is beautiful. It is important to not only learn the basic model of an SVM but also know how you can implement the entire model from scratch. This is a continuation of our series of tutorials on SVMs. In part1 and part2 of this series we discussed the mathematical model behind a linear SVM. In this tutorial, we’ll show how you can build an SVM linear classifier using the optimization routines shipped with Python’s SciPy library.
After completing this tutorial, you will know:
- How to use SciPy’s optimization routines
- How to define the objective function
- How to define bounds and linear constraints
- How