Hi R users!
I'm new to R, so I'm starting with a basic exercise in rpart.
I'm predicting if a user will churn based on past order history. I've
calculated the probabilities in excel, and if user is a single order
customer (1), then their probability of churn is 90%, if there are multiple
orders(0) then the probability of churning is 70%. In the R model, the
probability looks like it's 100% and 53%. In excel I used the count of
shopper_key to calculate probabilities. So I'm wondering if R has needs a
shopper_key to count?
It would be helpful if someone could suggest where I'm going wrong.
Thank you!
Code -
m1 <- rpart( churn ~ single_order , data = data2, method="anova" )
Output-
n= 22041
node), split, n, deviance, yval
* denotes terminal node
1) root 22041 3229.265 0.8216959
2) single_order< 0.5 8407 2092.852 0.5325324 *
3) single_order>=0.5 13634 0.000 1.0000000 *
shopper_key churn single_order
1 1 0
2 1 1
3 0 0
4 1 0
5 1 1
6 1 1
7 1 0
8 1 1
9 0 1
10 1 1
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