Random Forest Simple Explanation

Decision Tree: The Building Block

To understand the random forest model, we must first learn about the decision tree, the basic building block of a random forest. We all use decision trees in our daily life, and even if you don’t know it by that name, I’m sure you’ll recognize the process. To illustrate the concept, we’ll use an everyday example: predicting the tomorrow’s maximum temperature for our city. To keep things straight, I’ll use Seattle, Washington, but feel free to pick your own city.

Decision Process for Temperature Prediction
Machine Learning Decision Tree for Temperature Prediction

From Decision Tree to Random Forest

My prediction for the maximum temperature is probably wrong. And I hate to break it to you, but so is yours. There are too many factors to take into account, and chances are, each individual guess will be high or low. Every person comes to the problem with different background knowledge and may interpret the exact same answer to a question entirely differently. In technical terms, the predictions have variance because they will be widely spread around the right answer. Now, what if we take predictions from hundreds or thousands of individuals, some of which are high and some of which are low, and decided to average them together? Well, congratulations, we have created a random forest! The fundamental idea behind a random forest is to combine many decision trees into a single model. Individually, predictions made by decision trees (or humans) may not be accurate, but combined together, the predictions will be closer to the mark on average.


Machine learning may seem intimidating at first, but the entire field is just many simple ideas combined together to yield extremely accurate models that can ‘learn’ from past data. The random forest is no exception. There are two fundamental ideas behind a random forest, both of which are well known to us in our daily life:

  1. The wisdom of the (random and diverse) crowd



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