Don't forget your ML fundamentals/Rules that apply to ML also apply to DL

[Benjamin-Lee]

Have you checked the list of proposed rules to see if the rule has already been proposed?

• [x] Yes

Feel free to elaborate, rant, and/or ramble.

Maybe we could dedicate one rule to ML basics that people still don't seem to get right, which, although they are not DL specific, still apply to it. Issues #1, #19, #20, #21 all come to mind. This could possibly be rule one.

[agitter]

Machine learning applications in genetics and genomics is a good reference for ML basics in biology, in addition to the manuscript in #1.

[fmaguire]

One area I think would be good to add to this is more explicitly: a trained ML model itself (almost always) isn't the scientifically interesting thing in and of itself. It's what you do with it.

The scientifically important/relevant part happens is often using your inductive ML approach and identifying where the model works well/works poorly and digging deeply into the characteristics of these to make deductive inferences and hypotheses.

This is mentioned to a degree in https://github.com/Benjamin-Lee/deep-rules/blob/master/content/11.interpretation.md

But I think it would be good to make explicit either in this tip or the intro, if anything to try and stem the tide of papers which fit a model and call it a day adding very little to the field.