Suppose I want to build an interpretable model and the response variable is 0/1. However, there are 99% 1's and 1% 0's. The date looks like this:
week catA catB catC numvisits haspurchased 1 1 4 7 100 1
where catA catB and catC are categorical variables, and haspurchased is the response variable. catC has 1000 levels. So most of my independent variables are categorical. The ratio of 0's to 1's is 10,000 to 1. Here is my methodology:
dmSmote<-SMOTE(haspurchsed ~ . , data1,k=5,perc.over = 1400,perc.under=140) fit <- rpart(haspurchased ~., method="class", data=dmSmote)
Is this a viable approach? How do I know what parameters I choose for under and oversampling, and is there a way I can build an interpretable ensemble of trees instead on one tree?