Coding the powerful algorithm in python using (mainly) arrays and loops

This article aims to demystify the popular random forest (here and throughout the text —** RF**) algorithm and show its principles by using graphs, code snippets and code outputs.

The full implementation of the RF algorithm written by me in python can be accessed via: https://github.com/Eligijus112/decision-tree-python

I highly encourage anyone who stumbled upon this article to dive deep into the code because the understanding of the code will make any future documentation reading about **RF **much more straightforward and less stressful.

Any suggestions about optimizations are highly encouraged and are welcomed via a pull request on GitHub.

The building blocks of RF are simple decision trees. This article will be much easier to read if the reader is familiar with the concept of a classification decision tree. It is highly recommended to go through the following article before going any further:

#coding #machine-learning #random-forest #python #python from scratch #random forest algorithm