Parallelizing and cross-validating feature selection in R
This is an example piece of code for the Overfitting competition at kaggle.com. This method has an AUC score of ~.91, which is currently good enough for about 38th place on the leaderboard. If you read the completion forums closely, you will find code that is good enough to tie for 25th place, as well as hints as to how to break into the top 10.
However, I like this script because it does 2 tricky things well, without over fitting:
1. It selects features, despite the curse of dimensionality (250 observations, 200 features)
2. It fits a linear model, using the elastic net.
In future posts, I will walk you through how this code works, but for now, download the data and give it a shot!
To leave a comment
for the author, please follow the link and comment on his blog: Modern Tool Making
offers daily e-mail updates
news and tutorials
on topics such as: visualization (ggplot2
), programming (RStudio
, Web Scraping
) statistics (regression
, time series
) and more...
If you got this far, why not subscribe for updates
from the site? Choose your flavor: e-mail
, or facebook