Hi all, I am using the package "random forest" for random forest predictions. I like the package. However, I have fairly large data sets, and it can often take *hours* just to go through the "na.roughfix" call, which simply goes through and cleans up any NA values to either the median (numerical data) or the most frequent occurrence (factors). I am going to start doing some comparisons between na.roughfix() and some apply() functions which, it seems, are able to do the same job more quickly. But I hesitate to duplicate a function that is already in the package, since I presume the na.roughfix should be as quick as possible and it should also be well "tailored" to the requirements of random forest.
Has anyone else seen that this is really slow? (I haven't noticed rfImpute to be nearly as slow, but I cannot say for sure: my "predict" data sets are MUCH larger than my model data sets, so cleaning the prediction data set simply takes much longer.) If so, any ideas how to speed this up? Thanks! Mike "Telescopes and bathyscaphes and sonar probes of Scottish lakes, Tacoma Narrows bridge collapse explained with abstract phase-space maps, Some x-ray slides, a music score, Minard's Napoleanic war: The most exciting frontier is charting what's already here." -- xkcd -- Help protect Wikipedia. Donate now: http://wikimediafoundation.org/wiki/Support_Wikipedia/en [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.