Support Vector Machines: A Visual Explanation with Sample Python Code
SVMs are a popular classification technique used in data science and machine learning.
In this video, I walk through how support vector machines work in a visual way, and then go step by step through how to write a Python script to use SVMs to classify muffin and cupcake recipes.
In Part 1a, I visually define the following terms:
- Margin
- Support vectors
- Hyperplane
In Part 1b, I go through the following steps in a Jupyter Notebook:
- Import libraries (pandas, numpy, sklearn, matplotlib)
- Import data
- Prepare the data
- Fit the model
- Visualize results
- Predict a new case
In Part 2, I talk about ways to tune the model:
- Higher dimensions
- Multiple classes
- C parameter
- Kernel trick (RBF with gamma)
In Part 3, I talk about the pros and cons of SVM.
You can find all of my code and data on Github: https://github.com/adashofdata
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