Depth and OOB error of a randomForest and randomForestSRC - r

Question

Here is my code for random forest and rfsrc in R; Is there anyway to include n_estimators and max_depth like sklearn version in my R code ? Also, How can I plot OBB error vs number of trees plot like this?
set.seed(2234)
tic("Time to train RFSRC fast")
fast.o <- rfsrc.fast(Label ~ ., data = train[(1:50000),],forest=TRUE)
toc()
print(fast.o)
#print(vimp(fast.o)$importance)
set.seed(2367)
tic("Time to test RFSRC fast ")
#data(breast, package = "randomForestSRC")
fast.pred <- predict(fast.o, test[(1:50000),])
toc()
print(fast.pred)
set.seed(3)
tic("RF model fitting without Parallelization")
rf <-randomForest(Label~.,data=train[(1:50000),])
toc()
print(rf)
plot(rf)
varImp(rf,sort = T)
varImpPlot(rf, sort=T, n.var= 10, main= "Variable Importance", pch=16)
rf_pred <- predict(rf, newdata=test[(1:50000),])
confMatrix <- confusionMatrix(rf_pred,test[(1:50000),]$Label)
confMatrix
I appreciate your time.

Answer 1

You need to set block.size=1 , and also take note the sampling is without replacement, you can check the vignette for rfsrc:
Unlike Breiman's random forests, the default action here is sampling
without replacement. Thus out-of-bag (OOB) technically means
out-of-sample, but for legacy reasons we retain the term OOB.
So using an example dataset,
library(mlbench)
library(randomForestSRC)
data(Sonar)
set.seed(911)
trn = sample(nrow(Sonar),150)
rf <- rfsrc(Class ~ ., data = Sonar[trn,],ntree=500,block.size=1,importance=TRUE)
pred <- predict(rf,Sonar[-trn,],block.size=1)
plot(rf$err.rate[,1],type="l",col="steelblue",xlab="ntrees",ylab="err.rate",
ylim=c(0,0.5))
lines(pred$err.rate[,1],col="orange")
legend("topright",fill=c("steelblue","orange"),c("test","OOB.train"))
In randomForest:
library(randomForest)
rf <- randomForest(Class ~ ., data = Sonar[trn,],ntree=500)
pred <- predict(rf,Sonar[-trn,],predict.all=TRUE)
Not very sure if there's an easier to get ntrees error:
err_by_tree = sapply(1:ncol(pred$individual),function(i){
apply(pred$individual[,1:i,drop=FALSE],1,
function(i)with(rle(i),values[which.max(lengths)]))
})
err_by_tree = colMeans(err_by_tree!=Sonar$Class[-trn])
Then plot:
plot(rf$err.rate[,1],type="l",col="steelblue",xlab="ntrees",ylab="err.rate",
ylim=c(0,0.5))
lines(err_by_tree,col="orange")
legend("topright",fill=c("steelblue","orange"),c("test","OOB.train"))