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

Course Q&A Machine Learning Specialization Advanced Learning Algorithms
1

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!

2

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