Let's start with data description of the website visits I analyse :
- 6M rows
- Dependant variable
quotationis binary and takes values
1% of value 1
- The other 3 variables are
minuteof the day
The objective is to identify
quotation trend based on the weather to optimize communication campaigns and not to determine if for a given visit there will be a
To avoid overfitting problems due to the large dataset I decided to cross-validate my tree-models to determine the right one.
My questions :
Due to the low probability of
quotation = 1 even the best
leaf-node gets a
5% with the
training sample. Therefore, if I do a
predict() on my
Testing sample I get only
0 for all nodes.
- Is there a way with the
party packageto attribute the corresponding node to each value of the
- Is that the right method to evaluate my different models since
predict()doesn't seem to work for me (0 for all observations)?
I went there but every suggestions are based on
predict which is I feel of no help in my case...