Hi,
I have about half million binary features, and would like to find a model to estimate
the continous response. According to the inference, I can express predictors and
response by linear model. (ie. Design matrix: large sparse matrix with 0/1. Response:
Continous number) Since it is not a classification problem, someone suggested me to
try random forest in R. However, in the randomForest help page, it points out "For
large data sets, especially those with large number of variables, calling
'randomForest' via the formula interface is not advised: There may be too much
overhead in handling the formula." and I also gave a try on 300 variables and R either
gave me error message or no response. (OS: Windows XP; R:1.9.0 ; RAM:512MB) Is there
any way to implement random forest on this big dataset? Any suggestion is welcome!
Many thanks!
Chihying
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