I noticed that I can configure initial values for intercept and coefficient when using XGBRegressor - see below:
model= xgb.XGBRegressor(max_depth= 4, n_estimators= 50, verbosity= 0, tree_method= ‘exact’, n_jobs= self.cpus, booster= ‘gblinear’, objective= ‘reg:squarederror’, learning_rate= 0.10, gamma= 0.25, reg_alpha= 0.50, reg_lambda= 0.70, random_state= self.random_state, intercept_= pool_intercept, coef_= pool_coefficients)
My questions are the following: how does this command influence the way the regressor model is trained? is it normal to expect that the trained model will end with different intercept and coefficient values? Or, it will keep the same values?