On Thu, 16 Jun 2011, Jinrui Xu wrote:
Thanks for your feedback.
I think the problem is not because of many levels. There is only 1 column
with two levels as class labels in my input data.
Below is my code. The commandline "data.cforest.varimp <-
varimp(data.cforest, conditional = TRUE)" reports "Error in
model.matrix.default(as.formula(f),data = blocks): term 1 would require 4e+17
columns"
I also attached my input file. Hope you can run it for me to check what the
problem is. Thanks a lot!
PS: It takes 10 mins to finish the code below by 1 cpu and upto 2.5 GB
memory. You can reduce the columns in the rawinput, which reduces computing
intense and feeds back same error.
library(randomForest)
library(party)
set.seed(71)
rawinput <- read.table("featureSelection_rec.vectors")
rawinput$V1 <- as.factor(as.numeric(rawinput$V1))
data.controls <- cforest_unbiased(ntree=500, mtry=3)
data.cforest <- cforest(V1~.,data=rawinput,controls=data.controls)
data.cforest.varimp <- varimp(data.cforest, conditional = TRUE)
Hi Jinrui,
it turns out that for your data-set there are (using the default)
parameters 47 variables to condition on and thats way to much. You can
reduce the number of conditioning variables by increasing the `threshold'
parameter to something like .8
Best,
Torsten
there is a factor with (too) many levels in your data frame `rawinput'.
summary(rawinput)
will tell you which one.
Torsten
Quoting Torsten Hothorn <torsten.hoth...@stat.uni-muenchen.de>:
Hello everyone,
I use the following command lines to get important variable from training
dataset.
data.controls <- cforest_unbiased(ntree=500, mtry=3)
data.cforest <- cforest(V1~.,data=rawinput,controls=data.controls)
data.cforest.varimp <- varimp(data.cforest, conditional = TRUE)
I got error: "Error in model.matrix.default(as.formula(f),data = blocks):
term 1 would require 4e+17 columns"
I changed data dimension to 150. The problem still exists. So, I guess
there are other problems. Please give me some help or hints. Thanks!
jinrui,
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