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
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