Dear XGBoost folks,

I have 3 features, let’s say `x1`

, `x2`

, `s`

; and one target `Y`

I want to predict . You can imagine `x1`

and `x2`

are true covariates, `s`

is the variable we want to regress out.

In the linear model setting, people would like to have a `Y = intercept + x_1\beta_1 + x_2\beta_2 + s\beta_s + eps`

. Then the role of `intercept + x_1\beta_1 + x_2\beta_2`

would know.

I want to apply XGB to capture some non-linear information of `x1`

and `x2`

corresponding to `Y`

. Could I jointly optimize two XGBoost models such as `Y = XGB_1(x_1, x_2) + XGB_2(s)`

?

I am curious whether it is possible in XGBoost package or do you have any better suggestions？

Thanks!