Switch between 100K and 1M rows to compare plain boosting with XGBoost. XGBoost trains far faster with no loss in accuracy, and the gap grows with the data.
Same dataset, same idea of learning from mistakes. Pick a dataset size and compare how long each one takes to train.
Even on a small dataset, XGBoost finishes well ahead of plain Boosting, without giving up accuracy.
