Image by Todd Quackenbush For the past 30 years, tree-based algorithms such as Adaboost and Random Forests have been the go-to methods for solving tabular data problems. While neural networks (NNs) have been used in this context, they have historically struggled to match the performance of tree-based methods. Despite recent advancements in NN capabilities and their success in tasks from computer vision, language translation, and image generation, tree-based algorithms still outperform neural networks when it comes to tabular data. This article will introduce several reasons behind the continued dominance of tree-based methods in this domain.