Tree-Based Machine Learning Methods with the randomForestSRC Ecosystem

Tree-Based Machine Learning Methods: Prediction, Inference, and Variable Selection with the randomForestSRC Ecosystem

Virtual Short Course – cohosted by Mid-Missouri and Kansas-Western Missouri Chapters of the American Statistical Association

Date: Saturday, October 3, 2026

Time: 9:00am-12:00pm (CDT)

Format: Virtual

Registration Deadline: October 2, 2026 at 11:59pm

Fees:

Student: $25+ processing fee

Academic (non-student): $50 + processing fee

Non-academic: $75 + processing fee

Cancellation and Refund Policy: Refund deadline is September 26, 12 PM Central time. If tickets are refunded, processing fee will not be returned to the buyer.

Software: R packages: randomForestSRC • varPro • randomForestRHF (RHF) • randomForestSGT (SGT)

Material: Course materials and zoom login information will be distributed via email on October 2, 2026

Course Instructors:

• Dr. Hemant Ishwaran, Professor of Public Health Sciences, Graduate Program Director; Director of Statistical Methodology, Division of Biostatistics, University of Miami

• Dr. Min Lu, Research Associate Professor, Division of Biostatistics, University of Miami

Course Description: This half-day virtual workshop gives a practical, code-centered introduction to tree-based machine learning methods using R. Tree-based methods are useful for nonlinear signals, mixed data types, robustness, and scalable prediction. The workshop focuses on random forest ensembles and centers on the R-package randomForestSRC, which implements random forests for regression, classification, and survival. Topics include out-of-bag inference, test-data prediction, and variable selection using permutation VIMP, minimal depth, and rule-based variable priority with varPro. Advanced topics cover class imbalance, imputation and test time OOD, random hazard forests (RHF), and super greedy trees (SGT). Related packages in the randomForestSRC ecosystem will also be discussed.

Instructor Biographies

Dr. Hemant Ishwaran develops machine learning methods for complex biomedical and time-to-event data and turns them into practical open-source tools for investigators. He created Random Survival Forests and the R package randomForestSRC. His work has been applied in cardiovascular disease, heart transplantation, cancer, and genomics.

Dr. Min Lu works on random forests and trees, causal inference, variable selection, infectious disease modeling, statistical genomics, and meta-analysis. She develops methods and software in the randomForestSRC and varPro ecosystem and collaborates on practical applications in medicine and public health.

All times are US Central time.