So far, we’ve considered decision trees and random forest algorithms. We saw that random forest is a bagging (bootstrap aggregating) algorithm—it combines the output of multiple decision trees to give the prediction. Typically, in a bagging algorithm trees are grown in parallel to get the average prediction across all trees, where each tree is built on a sample of original data.
Gradient boosting , on the other hand, does the predictions using a different format. Instead of parallelizing the tree building process, boosting takes a sequential ...
