I've been training randomForest models on 7 million rows of data (41
features). Here's an example call:
myModel <- randomForest(RESPONSE~., data=mydata, ntree=50, maxnodes=30)
I thought surely with only 50 trees and 30 terminal nodes that the memory
footprint of "myModel" would be small. But it's
Message-
>> From: r-help-boun...@r-project.org
>> [mailto:r-help-boun...@r-project.org] On Behalf Of John Foreman
>> Sent: Wednesday, September 07, 2011 2:46 PM
>> To: r-help@r-project.org
>> Subject: [R] randomForest memory footprint
>>
>> Hello, I am att
Hello, I am attempting to train a random forest model using the
randomForest package on 500,000 rows and 8 columns (7 predictors, 1
response). The data set is the first block of data from the UCI
Machine Learning Repo dataset "Record Linkage Comparison Patterns"
with the slight modification that I
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