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!