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Shea and Anders dive into tree-based algorithms, starting with the most fundamental variety, the single decision tree. We cover the mechanics of a decision tree and provide a comparison to linear models. A solid understanding of how a decision tree works is critical to fully grasp the nuances of the more powerful ensemble models, the Random Forest and Gradient Boosting Machine. In addition, single decision trees can still be useful either as a starting point for building more complex models or for situations where interpretability is paramount.