What is Random Forest

Random Forest is an algorithm used widely in classification and regression problems. It builds many different Decision Trees and takes their majority vote for classification and average in case of regression.

Instead of using a single best tree, Random Forest builds many "weaker" trees. That may sound counterintuitive — why would we use models that are worse?

Think of it like this: a single decision tree is like a generalist — it tries to account for every feature and give you a complete picture. However, it can become too confident and make mistakes by overfitting to noise in the data.

A Random Forest, on the other hand, is like a team of specialists. Each tree is trained on different parts of the data and focuses on different aspects of the problem. Alone, each tree might not be very strong — it might even miss the bigger picture. But together, when you combine their "votes", they cover each other's weaknesses and provide a more balanced, accurate prediction.

You can also compare it to asking 100 competent students instead of relying on a single professor. While the professor might be more knowledgeable, even experts can be biased or misled. But if the majority of students independently arrive at the same answer, that consensus is often more robust.

In practice, combining many weaker Decision Trees into a single strong Random Forest works very well and often significantly outperforms a tuned individual Decision Tree on large datasets. The decision boundary of a Random Forest is smoother and generalizes better to new data than that of a single Decision Tree, so Random Forests are less prone to overfitting.

However, accuracy won't improve if we combine many models that make the same mistakes. For this approach to be effective, the models should be as different from each other as possible so that they make different errors.