In the context of tree-based ensemble methods, how does the Gradient Boosting technique specifically differ from Random Forests in terms of how individual trees are built to minimize model error?

Random Forests and Gradient Boosting are both ensemble methods that use multiple decision trees to make predictions, but they build these trees using fundamentally different logic. Random Forests use a process called bagging, which stands for bootstrap aggregating. In this approach, the algorithm creates several independent trees in parallel. Each tree is trained on a unique random subset of the data, created by sampling the original dataset with replacement. Because these trees are....