Site icon R-bloggers

New caret version with adaptive resampling

[This article was first published on Blog - Applied Predictive Modeling, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

A new version of caret is on CRAN now.

There are a number of bug fixes:

One big new feature is that adaptive resampling can be used. I’ll be speaking about this at useR! this year. Also, while I’m submitting a manuscript, a pre-print is available at arxiv.

Basically, after a minimum number of resamples have been processed, all tuning parameter values are not treated equally. Some that are unlikely to be optimal are ignored as resampling proceeds. There can be substantial speed-ups in doing so and there is a low probability that a poor model will be found. Here is a plot of the median speed-up (y axis) versus the estimated probability of model at least as good as the one found using all the resamples will occur.

The manuscript has more details about the other factors in the graph. One nice property of this methodology is that, when combined with parallel processing, the speed-ups could be as high as 30-fold (for the simulated example).

These features should be considered experimental. Send me any feedback on them that you may have.

To leave a comment for the author, please follow the link and comment on their blog: Blog - Applied Predictive Modeling.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.