Hey everyone,
I am trying to run a multiple-output Random Forest prediction using the randomForestSRC package using the rfsrc() command. I'm trying to run with ntree = 10, using my response variables [ 21 ] using a dataframe of [ 3670x217 ].
Line of code I'm trying to execute:
#training the RF model
rf_model <- rfsrc(response_formula_subset, data = train_data, na.action = "na.omit", ntree = 10)
R keeps showing this message with the symbol of a bomb saying R encountered a fatal error, needs to quite session and asking to restart the session.
I tried using a subset of the data (as you can see in the code, the subset includes only 5 responses). There would be roughly just under 200 predictors.
Really helpful if someone can explain why this is happening!
Thanks in advance!
Looks like a memory related problem. Have you tried with
save.memory = TRUE
?Just tried it, displaying the same error and R crashes
Can you watch memory utilisation as you run the model? Task manager on windows works.
Have you tried running this in a fresh, clean
R
session with no other (unnecessary) packages and data being loaded into memory? How much system memory do you have available? How much memory is free right beforerfsrc()
is invoked? As others here have stated,R
is probably running out of memory trying to execute the classifier.You might also want to consider sharing some data with us (using
[dput()][1]
) to help us try and replicate this issue ourselves.