# 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

• 1- There is no Ctree package. You are using the party package. 2- Clicks is a integer so I'm not sure why are you converting it to a categorical variable. 3- I don't think that Cost...converted.click is independant variable. It is actually a dependant variable of clicks. Feb 8, 2015 at 11:27
• I haven't used the party package. rpart has a simple cost function...as does C5.0. Would be surprised if there isn't a cost function hidden there somewhere, but if you have got lots of data could always downsample. Some might advocate using probabilities from ctree -not sure if that makes sense (ESL 9.2.5) Feb 9, 2015 at 1:46