# How to fit Decision Tree classifier for highly imbalanced response variable? [duplicate]

I use R, Party package in order to fit prediction model ("classifier") for

"Converted.clicks" as response variable.

The rest of vars are used as explaining variables in the model.

Here is the relevant part of my code:

table(DF$Converted.clicks) "0" = 31456 "1" = 39 "2" = 6 Formula<-Converted.clicks ~ Day.of.week + Device + Keyword + Quality.score + Network..with.search.partners. + Ad.group + Match.type ct<-ctree(Formula,data=DF) ####################################### ### Issue: The Converted.clicks variable is highly imbalanced.The majority of the observations has class "zero". So after ctree function is applied,all the predictions are "zero",there are no classes "1" and "2" predicted. ### My questions are: 1. Is the classifier Decision Tree model is appropriate model to predict as.factor(DF$Converted.clicks)?

2. If so, how can I balance the response var (i.e.to give the chance the two rest classes

"1" and "2" to be predicted?) - if I need to use weights, I need an