How to Interpret a Random Forest Model (Machine Learning with Python)

Machine Learning is a fast evolving field – but a few things would remain as they were years ago. One such thing is ability to interpret and explain your machine learning models. If you build a model and can not explain it to your business users – it is very unlikely that it will see the light of the day. Can you imagine integrating a model into your product without understanding how it works? Or which features are impacting your final result? In addition to backing from stakeholders, we as data scientists benefit from interpreting our work and improving upon it. The first article of this fast.ai I'm delighted to share part 2 of this series, which primarily deals with how you can intepret a random forest model. We will understand the theory and also implement it in Python to solidify our grasp on this critical concept.