In the regularization part of XGBoost objective function, it contains gammaT and also lambdasquare(W).
I understand gamma is the minimum node split criteria and T is number of leaves and regularizing them lead to a simpler model (not many splits/leaves).
However, I don't understand why regularizing /penalizing w will help in simpler model or what it does ? Since we are fitting regression trees for both regression and classification in XGBoost, how does having a small score on the leaf help with regularization of the tree structure? Or w is just there not regularize the tree structure but to keep the leaf weights low (if so, why)?
Thank you for any responses.