In What Scenario Would You Prefer a Decision Tree Over a Random Forest?

Machine learning can feel like magic when you first encounter it, especially when you hear terms like decision trees and random forests. Don’t worry; this blog will break everything down in simple terms. By the end of this article, you’ll know what these two techniques are, when to use each, and why sometimes a single decision tree is better than an entire random forest.

We’ll cover the following topics:

1. What is a decision tree?
2. What is a random forest?
3. Key differences between them.
4. When to prefer a decision tree over a random forest.
5. FAQs to answer common doubts.

Let’s start with the basics!

What is a Decision Tree?

A decision tree is a machine learning model that looks like a flowchart. Imagine you’re trying to decide if you should wear a jacket today. You might think:

• Is it cold outside?
• If yes, wear a jacket.
• If no, don’t wear a jacket.

This series of questions and answers is how a decision tree works. Each question, or node, helps split the data until it leads to a final decision, or leaf.