In this paper, we discuss how tree-based machine learning techniques can be used in the context of derivatives pricing. Gradient boosted regression trees are employed to learn the pricing map for a couple of classical, time-consuming problems in quantitative finance. In particular, we illustrate this methodology by reducing computation times for pricing exotic derivative products and American options. Once the gradient boosting model is trained, it is used to make fast predictions of new prices. We show that this approach leads to speed-ups of several orders of magnitude, while the loss of accuracy is very acceptable from a practical point of view. In addition to the predictive performance of these methods, we acknowledge the importance of interpretability of pricing models. For both applications, we therefore look under the hood of the gradient boosting model and elaborate on how the price is constructed and interpreted. The files provided here include: Training and test sets for both applications in the paper. Python scripts for training the models and making predictions with them.