I am trying to solve a classification problem on a highly imbalanced data set. I am using SMOTE to over sample the minority samples and down sample the majority ones. After creating a balanced data set, I applied the random forest model. But, the prediction error for the minority class is extremely high even after using a balanced data set. What could possibly be going wrong?library(DMwR)new.data <- SMOTE(Clicked ~ ., train, perc.over = 600, perc.under =  80)table(new.data$Clicked)rand.forest <- randomForest(Clicked ~., data=new.data, mtry = 7,                            importance = TRUE, proximity=TRUE, ntree = 1000                            )#confusion matrixtable(yhat.rf, test$Clicked)yhat.rf   0   1      0 889  47      1  57   7
oversampling plus down sampling using smote not working on random forests
machine learning;classification;r;unbalanced classes