<center>![Resources #18.png](https://steemitimages.com/DQmU9zDvLpkEUXKCFfmPuu6nfXGfo1cfpCYvhEUkTVBCRc9/Resources%20%2318.png)</center>
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Greg Tam, a data scientist in New York, published an article in the Pivotal Engineering Journal about interpreting decision trees and random forests. 

Those who are familiar with machine learning know that decision trees and random forests are among the popular algorithms for supervised learning. To better understand these algorithms, I also created 8 video tutorials as part of a series on machine learning on my Youtube channel (see [here](https://www.youtube.com/watch?v=9YcMzsFvfxU&t=8s&list=PLonlF40eS6nynU5ayxghbz2QpDsUAyCVF&index=11)).

To not divert from the main point, here's what Greg touches in his article:

- how decision trees work under the hood
- how to interpret a decision tree, via an illustrative example
- how to go from decision trees to random forests (using the same example)
- decision trees vs. random forests for classification (supervised learning)
- final thoughts. 

Additionally, the article includes graphs that augment the understanding of the example used by Greg. If you're interested, please follow the link below: 
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<center>[Interpreting Decision Trees and Random Forests](http://engineering.pivotal.io/post/interpreting-decision-trees-and-random-forests/)</center>
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[Cristi Vlad](http://cristivlad.com), Self-Experimenter and Author