Xgboost Regression Regularization. See description in the reference paper and XGBoost Tree Methods. In this approach trees are regularized using the complexity definition.
L2 Regularization in Light GBM. Ω f γ T 1 2 λ j 1 T w j 2. XGB uses the two kinds of regularization in both classification and regression.
In fact XGBoost is also known as a regularized boosting technique.
Controls whether a given node will split based on the expected reduction in loss after the split. XGB uses the two kinds of regularization in both classification and regression. Increasing this value will make model more conservative. So we will build an XGBoost model for this regression problem and evaluate its performance on test data unseen datanew instances using the Root Mean Squared Error RMSE and the R-squared R-coefficient of determination.
