Day 50: Multi-Class Classification with Logistic Regression
What We’ll Build Today
Extend binary logistic regression to handle multiple classes simultaneously
Implement One-vs-Rest (OvR) and Softmax strategies for multi-class problems
Build a news article categorizer that classifies articles into 4+ categories
Understand how Gmail assigns multiple priority levels and Netflix categorizes content into genres
Why This Matters:
From Binary Decisions to Complex Categorizationwith simple binary choices. Gmail doesn’t just filter spam—it also categorizes emails into Primary, Social, Promotions, Updates, and Forums. Netflix doesn’t just recommend or not recommend—it classifies content across dozens of genres. Tesla’s Autopilot doesn’t just detect “obstacle or no obstacle”—it identifies pedestrians, cyclists, vehicles, traffic signs, and lane markings.
Multi-class classification is the foundation for these sophisticated categorization systems. The same logistic regression principles you learned yesterday extend elegantly to handle 3, 10, or even 1000+ categories. This is how production AI systems at Google, Amazon, and OpenAI process millions of classification decisions per second.
Yesterday you learned to answer yes/no questions: spam or not spam, fraud or legitimate. But real AI systems rarely deal
