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Decision Trees
Introduction
Decision Tree
Where To Partition?
Information Gain
Another Look At Our Decision Tree
The Problem of Perturbations
Going Beyond A Single Tree
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Decision TreesIntroduction

Reading time: ~5 min

Imagine you're playing a game of "20 Questions". Your goal is to figure out what object your friend is thinking of by asking a series of simple questions. Every question you ask cuts the possibilities down, step-by-step, until you finally guess the right answer. This intuition is the foundation of one of the most popular algorithms in machine learning.

A Decision Tree operates on this exact same logic. It takes a complex dataset and breaks it down by asking a sequential series of questions, known as decision nodes. Each decision splits the data using a simple if−then rule.

As the data flows downwards, it eventually reaches the end of the line. The final endpoints are called leaf nodes, which represent the model's final .

To train a Decision Tree means teaching the algorithm to figure out the absolute best order in which to ask those questions, making it as efficient as possible at reaching the correct leaf.

Test your knowledge: If a Decision Tree is built successfully, what type of node sits at the very end of every path?

Decision Node
Root Node
Leaf Node

Why Decision Trees?

Decision Trees are wildly popular because they are incredibly easy to interpret. Unlike many complex algorithms that act as a "black box", you can trace a Decision Tree's logic from top to bottom and completely understand why it made a specific prediction.

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