2026-06-05 –, Grand Hall 1
Most machine learning methods give you a prediction but not a measure of how much to trust it. Bayesian Additive Regression Trees (BART) combine the flexibility of tree ensembles (e.g. random forests, boosting) with full uncertainty quantification—every prediction comes with a probability interval, not just a point estimate. This hands-on tutorial introduces BART through three applications: regression, classification, and survival analysis. Using pymc-bart, participants will learn to fit flexible models that automatically capture non-linear relationships while providing honest uncertainty estimates. We emphasize practical interpretation throughout: visualizing predictions with uncertainty bands, understanding variable importance, and interpreting model output.
Machine learning models are often evaluated on predictive accuracy alone, but accuracy without uncertainty can be misleading. Classical tree ensemble methods like random forests and gradient boosting provide point predictions, and while techniques like conformal inference or bootstrap aggregation can add uncertainty estimates, these are often poorly calibrated or computationally expensive.
Bayesian Additive Regression Trees (BART) offer a different approach: uncertainty quantification is built into the model, not ignored or bolted on afterward. BART models the response as a sum of small trees, with regularization priors that keep each tree weak. Posterior inference over the tree structures yields a full distribution over predictions—every fitted value comes with a credible interval that reflects genuine uncertainty about the underlying function.
This tutorial introduces BART through three applications, each demonstrating how uncertainty changes the way we interpret results:
Regression: We begin with continuous outcomes, fitting BART models and visualizing posterior predictive distributions. Rather than a single fitted curve, participants will see HDI bands that widen where data is sparse and narrow where evidence is strong. We'll explore variable importance—which comes with its own uncertainty—and partial dependence plots that reveal non-linear effects.
Classification: For binary outcomes, BART produces predicted probabilities with uncertainty, not just class labels. We'll examine how this uncertainty propagates through decision-making and compare calibration against standard classifiers.
Survival analysis: Time-to-event data is inherently uncertain, and BART's flexibility is particularly valuable when the hazard function has unknown shape. Participants will fit survival models and plot individualized survival curves with credible intervals—essential for communicating risk to stakeholders.
Target audience
Data scientists and analysts looking to add useful statistical methods to their toolkit.
Takeaways
Participants will leave able to fit BART models for continuous, binary, and time-to-event outcomes; interpret predictions with full posterior uncertainty; use variable importance and partial dependence plots appropriately; and decide when BART's uncertainty quantification justifies its computational cost over simpler alternatives.
Materials
GitHub repository with marimo notebooks, real-world datasets from sports, psychology, and other domains, environment files, and a one-page BART reference guide. Participants should clone the repository and verify their setup before the session.
Chris is a Principal Quantitative Analyst at PyMC Labs and an Adjoint Associate Professor at the Vanderbilt University Medical Center, with 20 years of experience as a data scientist in academia, industry, and government, including 7 years in pro baseball research with the Philadelphia Phillies, New York Yankees, and Milwaukee Brewers.
He is interested in computational statistics, machine learning, Bayesian methods, and applied decision analysis. He hails from Vancouver, Canada and received his Ph.D. from the University of Georgia.