Machine Learning can sound overwhelming at first, but some algorithms are surprisingly easy to understand. One such algorithm is the Decision Tree — a simple yet powerful tool for making predictions. If you’re new to the world of ML, think of a decision tree like a flowchart that guides you step-by-step toward a decision. In this article, we’ll break down how the decision tree algorithm works in the easiest way possible.
What is a Decision Tree?
A Decision Tree is a supervised learning algorithm used for classification and regression tasks. It works by asking a series of yes/no questions (called decisions) that split the dataset into smaller parts, helping it reach a final output (prediction).
Think of it like playing a game of “20 Questions” to guess an object, but instead, the goal is to predict something like whether an email is spam or not, or whether a customer will buy a product.
How Does a Decision Tree Work?
Let’s understand the decision tree through an example.
We have a dataset like this:
Our goal: Predict whether someone will play tennis or not based on the conditions.
The decision tree will:
- Choose the best attribute to split the data.
- Split the dataset into subsets.
- Repeat the process for each subset until it reaches a decision.
Let’s break these steps down further.
1. Start at the Root Node
This is the top of the tree. The algorithm looks at all the features (Outlook, Temperature, etc.) and decides which feature splits the data best.