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$Clicked) rand.forest <- randomForest(Clicked ~., data=new.data, mtry = 7, importance = TRUE, proximity=TRUE, ntree = 1000 ) #confusion matrix table(yhat.rf, test$Clicked) yhat.rf 0 1 0 889 47 1 57 7