Chi_Square_Feature_Selection_Complete_Guide

Chi-Square Test (χ²) for Feature Selection - Beginner’s Complete Guide

📋 Table of Contents

1. Foundation: Understanding the Basics
2. Core Concepts Explained
3. When and Why to Use Chi-Square
4. Mathematical Foundation (Simplified)
5. Data Preparation: Critical Steps
6. Sklearn Implementation - Deep Dive
7. Complete Workflow with Detailed Explanations
8. Practical Examples with Full Code
9. Interpretation Guide
10. Troubleshooting Common Issues
11. Best Practices and Tips

Foundation: Understanding the Basics

What is Feature Selection?

Before diving into Chi-Square, let’s understand why we need feature selection:

The Problem:

• You have a dataset with 50 columns (features)
• Some features are useful for predictions, others are not
• Using all features can lead to:
• Overfitting: Model memorizes noise instead of learning patterns
• Slow training: More features = more computation time
• Poor performance: Irrelevant features confuse the model