5.1.1. Machine Learning Gone Wrong

But just because you can, doesn’t mean you should.

The classic citation for this argument is from Jurassic Park.

There are many examples of ML applied wrong and practitioners in the space that I talk to spend a lot of time keeping their data science teams from replicating some notable breakdowns:

Common problems with analysis (ML or otherwise)



The good news is that these problems can be avoided. Understanding how is something we will defer until we have a better understanding of the methods and processes we will follow in an ML project.