A variable with high separate power doesn't improve ROC curve

Dear expert,

I’m currently use BDTG to for classification.
we use 8 variables to discriminate signal and background, I try to add a new variable.
The new variable has the highest separation power and highest variable importance among these 9 variables, but the ROC curve doesn’t improve at all, what could be the issue?
From the Gui, the smirnov test shows BDTG is not overtrained.

Thanks in advance,

Dear Binghuan,

There are various possible reasons for this, among which is that the individual separation power of a single variable is not an indicator of the overall classifier performance made from a collection of variables. It’s possible that the information provided by other variables standalone makes up for the individual performance of this variable.



Dear Sergei,

Thank you for a prompt reply.
I also checked the correlation between the new variable and other 8 variables, they all are less than 20 %.
So I doubt the information provided by other variables can make up for this variable.
Are there any other reasons to explain this or any checks I can do?