![]() That’s what the forest part means if you put together a bunch of trees, you get a forest. A random forest is a bunch of different decision trees that overcome overfitting With that in mind, let’s first understand what a random forest is and why it’s better than a simple decision tree. The reason for this is simple: in a real-life situation, I believe it’s more likely that you’ll have to solve a classification task rather than a regression task. I’d like to point out that we’ll code a random forest for a classification task. Note: this article can help to setup your data server and then this one to install data science libraries. Also, having matplotlib, pandas and scikit-learn installed is required for the full experience. ![]() For reading this article, knowing about regression and classification decision trees is considered to be a prerequisite. In this tutorial, you’ll learn what random forests are and how to code one with scikit-learn in Python. If you already know how to code decision trees, it’s only natural that you want to go further – why stop at one tree when you can have many? Good news for you: the concept behind random forest in Python is easy to grasp, and they’re easy to implement. ![]()
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