= 27d567bc2d48d9f40e9ccc20ea370b15)
pdf23ds comments on Modeling sleep patterns - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (46)
I have six months of past sleep data, though nothing current, with sleep and wake times. I could easily augment that with other potentially relevant variables, like daily caffeine intake or whatnot.
As a starter method, I would try Adaboost. AdaBoost is nice because it is easy to implement, gives some protection against overfitting, and allows you a lot of liberty to define whatever context functions/predictors you want. Try to predict whether a given hour will be sleep or not. Use whatever information like caffeine intake you can as predictors, and use as many of them as you can dream up: AdaBoost will figure out which ones are the most important.
Helpful, thanks.
Wake and sleep times don't strike me as very good data, IMO. If you had 6 months of Zeo data, that'd be real data you could try to feed into a model of some sort.