Interpretation of regression coefficients for Linear Booster


I am using XGBRegressor for multiple linear regression.

xgb_model = XGBRegressor(n_estimators=10, learning_rate=0.06, gamma=1, booster='gblinear', 
                     reg_lambda=0.0001, reg_alpha=0.0001,

I am getting the coefficients using xgb_model.coef_. Would the interpretation of the coefficients be the same as that of OLS Regression? That is, they represent “the mean change in the response variable for one unit of change in the predictor variable while holding other predictors in the model constant.”


Yes, since the model is of form y = X * beta.