I have customer data for around 400,000 customers where 270,000 of them are current customers and 130,000 of them are past customers who churned, what I am doing is classifying them as 0 (non-churn) and 1 (churner) to come up with probabilities for likelihood of churning. I am using random forests in R.
What I want to know is can I use the full training set (splitting 80/20 for train and test sets) then use the entire current customer list to output the probabilities or will using the same data as the training/testing data affect the final output?
Should I instead take a sample of current and past customers and not include that in the final output of the model? I need to use some current customer data to train the model but can I still use that same data to output the churn risk?