# Misclassification Rate for Random Forest Plateauing too Early

Using R, I have created 5 different random forest models using 5 different numbers of trees (3,10,30,100,300). My intention was to compute the misclassification rates of each of these models and plot the rates against the number of trees to illustrate the idea that generally, an increase in trees in a random forest model correlates with a decreasing misclassification rate.

I had a few colleagues run this same model in Python and with all of them, their model reached a misclassification rate of ~0.08 with the 300-tree model. However, When I run my models in R, the misclassification rate seems to level out around ~0.2 at the 100-tree model, and does not get any lower with the ~300 tree model. I'm curious as to what may be causing this discrepancy. I've provided my code below.

madelon_train <- data.frame(madelon_train_data, madelon_train_labels)
for(i in c(3,10,30,100,300)){
randomForest(as.factor(madelon_train$V1.1) ~ ., data = madelon_train, ntree = i, mtry = sqrt(500), replace = FALSE)) } modellist <- vector(mode="list", length=5) for(i in c(3,10,30,100,300)){ modellist[[i]] <- eval(as.name(paste("madelonforest", i, sep = ""))) } #Use models to predict training data and compute misclassification error classerrlisttrain <- vector(mode="list", length=5) for(i in c(3,10,30,100,300)){ err <-table(as.numeric(as.character(predict(modellist[[i]], madelon_train_data, type = 'class', OOB = TRUE))) - madelon_train_labels) classerrlisttrain[[i]] <- assign(paste("misclassification", i, sep = ""), err[names(err)==0]) } for(i in c(3,10,30,100,300)){ classerrlisttrain[[i]] = as.double(classerrlisttrain[[i]]) classerrlisttrain[[i]] = 1 - classerrlisttrain[[i]]/length(madelon_train_labels$V1)
}

#Use models to predict test data and compute misclassification error

classerrlisttest <- vector(mode="list", length=5)
for(i in c(3,10,30,100,300)){
err <-table(as.numeric(as.character(predict(modellist[[i]],
classerrlisttest[[i]] <- assign(paste("misclassification", i, sep = ""),
err[names(err)==0])
}

for(i in c(3,10,30,100,300)){
classerrlisttest[[i]] = as.double(classerrlisttest[[i]])
classerrlisttest[[i]] = 1 -
}

#Plot misclassification errors vs Tree Depth

plot(c(3,10,30,100,300), classerrlisttrain[c(3,10,30,100,300)], type = 'l',
xlab = 'Number of Trees', ylab = 'Misclassification Rate', xlim = c(1,300),
ylim = c(0,0.5), col = "red")
lines(c(3,10,30,100,300), classerrlisttest[c(3,10,30,100,300)], type = 'l',
col = "blue")
legend(1,0.1,legend = c("Train Data", "Test Data"), col =
c("red","blue"),lty=1, cex=0.8)