Random ForestEnsemble Learning

Imagine you have to guess the exact number of jellybeans in a massive glass jar. If you ask just one person, their guess might be way off. But if you ask 100 random people, some will guess way too high and some way too low. Amazingly, if you average all their guesses together, the final number is usually incredibly accurate!

This is the core idea behind Ensemble Learning. Instead of relying on a single, potentially flawed model (like one deeply overfitted Decision Tree), we train a massive group—or "ensemble"—of weaker models. By combining their predictions through a simple majority vote, the group collectively smooths out individual mistakes and produces a much stronger, more accurate result.

This concept is famously supported by Condorcet's Jury Theorem, which mathematically proves that if each voter is even slightly more likely than not to be right, a majority vote of a large group guarantees an almost perfect decision!

Test your knowledge: What does an ensemble model rely on to make its final prediction?

To see this in action, we are going to build our own Random Forest model to solve a real-world problem: classifying whether a road sign is a Pedestrian Crossing sign or not.

Road signs come in all shapes and sizes, so our forest will focus on four simple features:

• Size
• Number of sides
• Number of colors used
• Whether the sign contains text or symbols

To begin building our forest, we first need to learn a clever data-sampling trick called the Bagging Method.