step necessary
The 4 steps necessary before fitting a machine learning model
There are many steps in a common machine learning pipeline and much thought that goes into architecting it. There is the problem definition, data acquisition, error detection and data cleaning, etc. In this story, we begin with the assumption that we have a clean and ready to go dataset. With that in mind, we outline the four steps necessary before fitting any machine learning model. We then implement those steps in Pytorch, using a common syntax for invoking multiple method calls; method chaining.