[prestonh]

This 'assumption' always bothered me when studying for DS roles because it's something that you're expected to know if asked, but isn't really true/accurate. Another is the non-collinearity assumption between variates, which is violated all the time in ML tasks but an 'assumption' of the model nonetheless.

[closed]

In general, it's helpful for me to separate assumptions that are characteristics of the generative model (and possibly an inference procedure used with it), and "assumptions" meaning things that could lead to poor out of sample prediction.