Predictive analytics reaches out into more and more areas of business, industrial, and research applications. The sheer number of different algorithms and technologies is staggering. In this presentation we give a top-level review of the most popular tree-based algorithms now easily available to anyone through Minitab. From individual trees to powerful modern ensembles, we highlight their strengths, weaknesses, uses, and limitations, especially when compared to the conventional modeling techniques like multiple linear and logistic regression. We illustrate the flexibility of the modern tree-based predictive analytics by building a series of models to predict power generation of a solar energy power plant. In this case, gradient boosting ensemble achieves 20% more accuracy compared to the conventional regression. In addition, the winning model provides great insights into the nature of multivariate dependencies.