I'm trying to build an xgboost regressor or a catboost regressor for a task. I have a working linear regression model. I also trained an xgboost regressor model for the task but it was worse than the linear regression model. I am wondering if there is a way to pass the linear regression weights (model parameters) as an initial set of parameters to the xgboost (or catboost) model to ensure performance gain?
E.g. if $w0*x0 + w1*x1 + w2*x2 + w3*x3 = y$ is the linear regression model, is there any way to tell xgboost to start by the same equation (and get better while training)?