Dear All, Please consider the snippet at the end of the email The output of the randomForest model is rf1 (i.e. the trained model). Now, is there a way to save this rf1 so that, in a different R session, I can just load it without repeating the analysis? Of course I have in mind much more CPU-intensive simulations than in this artificial example (where it would be really unpractical to recalculate everything and the model has to be applied to different data sets at different time or needs to be combined with other models). Any suggestion is welcome. Cheers Lorenzo ###################################### # simulate the data x1=rnorm(1000) x2=rnorm(1000,x1,1) y=2*x1+rnorm(1000,0,.5) df=data.frame(y,x1,x2,x3=rnorm(1000),x4=rnorm(1000),x5=rnorm(1000)) # run the randomForest implementation library(randomForest) rf1 <- randomForest(y~., data=df, mtry=2, ntree=50, importance=TRUE) ####################################
Saving a Random Forest Model
2 messages · Lorenzo Isella, Rui Barradas
Hello, ?save can save any R object. To load it back into R, use ?load. Hope this helps, Rui Barradas Em 23-03-2013 10:35, Lorenzo Isella escreveu:
Dear All, Please consider the snippet at the end of the email The output of the randomForest model is rf1 (i.e. the trained model). Now, is there a way to save this rf1 so that, in a different R session, I can just load it without repeating the analysis? Of course I have in mind much more CPU-intensive simulations than in this artificial example (where it would be really unpractical to recalculate everything and the model has to be applied to different data sets at different time or needs to be combined with other models). Any suggestion is welcome. Cheers Lorenzo ###################################### # simulate the data x1=rnorm(1000) x2=rnorm(1000,x1,1) y=2*x1+rnorm(1000,0,.5) df=data.frame(y,x1,x2,x3=rnorm(1000),x4=rnorm(1000),x5=rnorm(1000)) # run the randomForest implementation library(randomForest) rf1 <- randomForest(y~., data=df, mtry=2, ntree=50, importance=TRUE) ####################################
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