You've played 20 questions before. You think of something. Someone asks yes/no questions to figure out what it is. Is it alive? Is it bigger than a car? Does it have legs?

Each question narrows down the possibilities until they get to the answer.

A decision tree does exactly that. It learns a series of yes/no questions from your data. Then it uses those questions to classify new examples.

It's one of the most intuitive ML models out there. You can actually look at it and understand every decision it makes. That's rare.

What You'll Learn Here

How a decision tree builds itself by asking questions

What entropy and information gain are (plain English, no scary math)

How to build and visualize a decision tree with scikit-learn

Why trees overfit so badly and how to control it

How to read feature importance from a tree

How a Tree Makes a Decision

Imagine you're trying to classify whether someone will buy a product based on their age and income.

A decision tree might learn this logic:

Is income > 50k?…