Executive Overview of Random Forest Models

Author(s): Adam Ross Nelson

Originally published on Towards AI.

Seedling to Canopy: Growth Random Forests in Machine Learning

At the top of this article is a reminder that you don’t have to be a computer scientist to do data science. Actually, you don’t need to be a data scientist to do data science. Depending on the nature of the problem you’re trying to solve, you may be quite effective as you use tools and principles rooted in data science.

This premise is true even if your level of proficiency in statistics, programming, and data science may still be developing.

Image Credit: Author’s Illustration created in Canva.

Let me make some additional room and some additional space for some folks who are newer in the learning journey. As it turns out, every once in a while, readers of my articles will share coding ideas with me that I have yet to see or use myself. If you’re more towards the later end of your learning journey and you see me doing something odd that doesn’t make sense or if you see a better way, feel free to pipe up in the comments.

As you read, if you feel a bit lost by a key term, there is a glossary at the bottom of this article.

First, this article provides an overview of random forests,… Read the full blog for free on Medium.

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Published via Towards AI