brainhack-school2020 / elised_blup_brain-learning-unicorn-project Goto Github PK

Hi! I would recommend following the suggested nomenclature for the projects: (githubname)_(projectname) So your project would be Elise-douard_BLUP 😄 Long live unicorns!

Hey @illdopejake (and/or other?) !

I just finished to run a random forest model for this project with a script inspired by your dear presentation on SVR model. (Thanks for this great content!)

The features are the volumes for 68 regions in > 35 000 brains
And the labels are 2 groups highly imbalanced ...

I get super-suspicious r2 and MAE with my final set:
accuracy (r2) = 0.96
MAE = 0.035

I also "tweeked" the depth parameter to see if there was something wrong on this side, but it seems that the value choose (10) was the best (after, there is an over-fitting, see figure bellow)

Can these perfect r2 and MAE be due to the large amount of data (n > 35 000 brains to feed this model) or the imbalances groups?
Is there a specific step to do for random forest?
Or can I publish in Nature now? :p

Thanks !

Hi Elise, your project seems really interesting. I was wondering whether your dataset contains only brains with pathogenic genetic variant? If so, will your model be more biased towards the pathogenic genetic variant?

Looks like an interesting project! I'm curious about the brain measures you'll use. Does the UK Biobank have derived brain measures like cortical thickness, or will you have to compute your brain measures yourself?