diff --git a/R/idiomclassification_mk_pf.R b/R/idiomclassification_mk_pf.R
index 47d4ef9..c1f4dbc 100644
--- a/R/idiomclassification_mk_pf.R
+++ b/R/idiomclassification_mk_pf.R
@@ -58,6 +58,8 @@
 train <- ngramme[trainRows,]
 test <- ngramme[setdiff(1:nrow(ngramme),trainRows),]
 
+cat("Random Forest without SMOTE\n")
+
 rf_classifier = randomForest(fmla, train, importance=TRUE)
 
 # only SY features
@@ -65,24 +67,19 @@
 
 prediction_for_table <- predict(rf_classifier, test %>% select(-CO_IDIOM))
 
-# different cutoff for prediction
-# prediction_for_table <- predict(rf_classifier, test %>% select(-CO_IDIOM), cutoff = c(0.8, 0.2))
-
 res <- confusionMatrix(prediction_for_table, test$CO_IDIOM,positive= "1")
-cat("Without SMOTE")
 print(res)
 
 # Sensitivity is recall of class 1
 # Pos Pred Value is precision
 varImpPlot(rf_classifier)
 
-# optional resampling with smote
+cat("With SMOTE resampled training data\n")
 
 smoted.data <- SMOTE(fmla, subset(train, select = c("CO_IDIOM", vars)), perc.over = 1200, perc.under = 100)
 rf_classifier = randomForest(fmla, smoted.data, importance=TRUE)
 prediction_for_table <- predict(rf_classifier,test %>% select(-CO_IDIOM))
 res <- confusionMatrix(prediction_for_table,test$CO_IDIOM, positive = "1")
-cat("With SMOTE")
 print(res)
 
 # Using estimates by random forest on entire dataset