Decision Trees are one of the simplest yet most powerful algorithms in machine learning. But how do they actually work under the hood?
In this article, I break it down using a small toy dataset to walk through the entire process of building a decision tree by hand. No frameworks, no shortcuts — just pure logic.
You'll learn:
What entropy and information gain are
How to choose the best features for splitting
How to stop the tree from overgrowing
Step-by-step math behind the splits
Whether you're a beginner or brushing up on fundamentals, this hands-on approach will give you a deeper understanding of how classification trees work.
📖 Read the full article on Medium
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