4 ms·
Very interesting littlw study. The results seem to make sense, and I'm impressed that his model can learn what causes his weight swings given the low resolution
by mbrundle 10y ago
Very interesting littlw study. The results seem to make sense, and I'm impressed that his model can learn what causes his weight swings given the low resolution delta-weight data he collects.
The author uses vowpal-wabbit to train his regression model. Anybody know what learning algorithm it uses (eg random forest?) Here's the link: https://github.com/JohnLangford/vowpal_wabbit/wiki https://github.com/JohnLangford/vowpal_wabbit/wiki
- Terribledactyl 10y agoSeems to be a slightly modified Stochastic Gradient Descent to handle the data typically seen with this sort of analysis. https://github.com/JohnLangford/vowpal_wabbit/wiki/Learning-algorithm https://github.com/JohnLangford/vowpal_wabbit/wiki/Learning-...
- ariel-faigon 10y agoThanks mbrundle. I'm the person behind that git repository and honestly am in a bit of a shock that this is making hacker-news. As I say in the README.md: please ignore the noise, the scales I used had 0.2 pound resolution, and my data-set was too small (and as one snarky commenter noticed, some words were misspelled). What is important is the big picture. There are actually numerous contradictions and irregularities in the data. In particular, any food item that appears only once or twice in the data-set, and is randomly coinciding with other features that make it biased the wrong way contributes to the error of the model. So as I say in the README, I would ignore anything that's not near the top or bottom, and even those should be taken with a healthy dose of (noise/modeling) skepticism. Anyway, the code is free for everyone to use so people are encouraged to run the experiment on themselves using more accurate methods and contributing more data. It only requires R+ggplot2 and vowpal wabbit. Cheers.