Is Regularization Effective At Reducing Overfitting in Decision Trees (xgboost)?

In the first course, we explored how regularization reduces overfitting by decreasing the values of the model parameters.

Im curious to know if the same regularization process as taught in course 1 is an effective method to reduce overfitting in an xgboost model? Can anyone explain the intuition here? Thanks for reading!

Yes by setting the maximum depth of the tree or the min sample splits you can avoid overfitting in the tree structure as well.