Apache Spark's Machine Learning Library (MLlib) is designed primarily for scalability and speed by leveraging the Spark runtime for common distributed use cases in supervised learning like classification and regression, unsupervised learning like clustering and collaborative filtering and in other cases like dimensionality reduction. In this article, I cover how we can use SHAP to explain a Gradient Boosted Trees (GBT) model that has fit our data at scale. Before we understand what Gradient Boosted Trees are, we need to understand boosting. Boosting is an ensemble technique that sequentially combines a number of weak learners to achieve an overall strong learner. In case of Gradient Boosted Trees, each weak learner is a decision tree that sequentially minimizes the errors (MSE in case of regression and log loss in case of classification) generated by the previous decision tree in that sequence.