Random Forest Algorithm in Python from Scratch

Random Forest Algorithm in Python from Scratch

Random Forest Algorithm in Python from Scratch. 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.

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

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