While the XGBoost model often achieves higher accuracy than a single decision tree, it sacrifices the intrinsic interpretability of decision trees.  For example, following the path that a decision tree takes to make its decision is trivial and self-explained, but following the paths of hundreds or thousands of trees is much harder. To achieve both performance and interpretability, some model compression techniques allow transforming an XGBoost into a single "born-again" decision tree that approximates the same decision function
Given this paragraph, what are the potential advantages or disadvantages of using XGBoost versus a single decision tree?
One advantage of using XGBoost over a single decision tree is that it frequently achieves higher accuracy. One disadvantage is that it sacrifices interpretability because it is much more difficult to follow the path of hundreds or thousands of trees.