  XGBoost, a scalable tree boosting algorithm, has proven effective for many
prediction tasks of practical interest, especially using tabular datasets.
Hyperparameter tuning can further improve the predictive performance, but
unlike neural networks, full-batch training of many models on large datasets
can be time consuming. Owing to the discovery that (i) there is a strong linear
relation between dataset size & training time, (ii) XGBoost models satisfy the
ranking hypothesis, and (iii) lower-fidelity models can discover promising
hyperparameter configurations, we show that uniform subsampling makes for a
simple yet fast baseline to speed up the tuning of large XGBoost models using
multi-fidelity hyperparameter optimization with data subsets as the fidelity
dimension. We demonstrate the effectiveness of this baseline on large-scale
tabular datasets ranging from $15-70\mathrm{GB}$ in size.
