The Department of Defense (DOD) has a considerable interest in forecasting key quantities of interest including demand signals, personnel flows and equipment failure. Many forecasting tools exist to aid in predicting future outcomes, and there are many methods to evaluate the quality and uncertainty in those forecasts. When used appropriately, these methods can facilitate planning and lead to dramatic reductions in costs. This talk explores the application of machine-learning algorithms, specifically gradient-boosted tree models, to forecasting and presents some of the various advantages and pitfalls of this approach. We conclude with an example where we use gradient-boosted trees to forecast Air National Guard personnel retention.