I am sorry for posting this question again but I really need help on this now. I am trying to calculate the AUC of training set of randomForest model in R and there are two ways to calculate this but give different results. The following is a reproductible example of my question. I really appreciate it if someone could help!!!
library(randomForest)
library(pROC)
library(ROCR)
# prep training to binary outcome
train <- iris[iris$Species %in% c('virginica', 'versicolor'),]
train$Species <- droplevels(train$Species)
# build model
rfmodel <- randomForest(Species~., data=train, importance=TRUE, ntree=2)
#the first way to calculate training auc
rf_p_train <- predict(rfmodel, type="prob",newdata = train)[,2]
rf_pr_train <- prediction(rf_p_train, train$Species)
r_auc_train1 <- performance(rf_pr_train, measure = "auc")@y.values[[1]]
r_auc_train1 #0.9888
#the second way to calculate training auc
rf_p_train <- as.vector(rfmodel$votes[,2])
rf_pr_train <- prediction(rf_p_train, train$Species);
r_auc_train2 <- performance(rf_pr_train, measure = "auc")@y.values[[1]]
r_auc_train2 #0.9175
To receive the same results for both prediction functions you should exclude the newdata parameter from the first one (explained in the package documentation for the predict function),
rf_p_train <- predict(rfmodel, type="prob")[,2]
rf_pr_train <- prediction(rf_p_train, train$Species)
r_auc_train1 <- performance(rf_pr_train, measure = "auc")@y.values[[1]]
r_auc_train1
returns,
[1] 0.8655172
The second function returns the OOB votes as explained in the package documentation of the randomForest function,
rf_p_train <- as.vector(rfmodel$votes[,2])
rf_pr_train <- prediction(rf_p_train, train$Species);
r_auc_train2 <- performance(rf_pr_train, measure = "auc")@y.values[[1]]
r_auc_train2
returns (the same result),
[1] 0.8655172