A Walkthrough of the Machine Learning Life Cycle
Do you have a project idea but you don’t know where to start? Or maybe you have a dataset and want to build a machine learning model, but you’re not sure how to approach it?
In this article, I’m going to talk about a conceptual framework that you can use to approach any machine learning project. This framework is inspired by the theoretical framework and is very similar to all of the variations of the machine learning life cycle that you may see online.
So why is a framework important?
A framework in machine learning is important for a number of reasons:
- It creates a standardized process to help guide one’s data analysis and modeling
- It allows others to understand how a problem was approached and fix older projects
- It forces one to think more deeply about the problem they are trying to solve. This includes things like what the variable is that will be measured, what the limitations are, and potential problems that might arise.
- It encourages one to be more thorough in their work, increasing the legitimacy of the findings and/or end result.
With these points in mind, let’s talk about the framework!
