I basically have something like:
xgbmod = xgboost(data = data.matrix(train_s), booster = "gblinear", nround = 100, #max_depth=10, lambda=0.5, label = data.matrix(train_df$ss), objective = "reg:squarederror")
You can see the current objective function is squared error loss.
I want to define a new objective function:
(pred - true)^2 + penalty*(a variable in my data)
So I want to penalize on a variable in my data.
How can I change the objective function to this using XGboost function in R? Is there a way that to define the loss function without touching the source code of it. Because the pred is changing in the loss, as we have the penalty term, and I think we cannot use any existing model.
Thanks in advance!!