One of the things I am trying to do this year is some more technical posts (following up on some issues I have noticed at the intersection between medicine and machine learning).

This is the first in a little mini-series on testing. Medical research has a different way of doing things, being more cautious about making claims and a bit more rigorous in justifying them, both of which are useful ideas to apply more broadly in machine learning (particularly at the applied end).

While performance testing is often considered basic knowledge, one of my supervisors/colleagues is a bit of a expert so I hope I can pass on some new ways of looking at things that are interesting even for some of the more knowledgeable folks around here.



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