Finding the Tallest Tree: Comparing Decision Tree, Random Forest & Boosted Tree Packages by Jared P. Lander. Visit https://rstats.ai/nyr/ to learn more. Abstract: There are many ways to fit tree-based models in R, including the rpart, randomForest and xgboost packages. We compare their user interfaces and results to judge them on usability and accuracy. Bio: Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone, a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world. He is a member of the Strata New York selection committee. His writings on statistics can be found at jaredlander.com. Twitter: https://twitter.com/jaredlander Presented at the 2020 R Conference | New York (August 14th, 2020)