Tree-Based Machine Learning Methods for Prediction and Variable Selection

The American Statistical Association (ASA) is the world’s largest community of statisticians, the “Big Tent for Statistics.” Since it was founded in Boston in 1839, the ASA has supported excellence in the development, application, and dissemination of statistical science throughmeetings,publications,membership services,education (including short courses such as this one),accreditation, andadvocacy.Our members serve in industry, government, and academia in more than 90 countries, advancing research and promoting sound statistical practice to inform public policy and improve human welfare.As a local chapter of the ASA, the Cincinnati Chapter's purpose is to encourage the use and appreciation of statistics and to promote its proper application in all fields of study. We also strive to facilitate interaction among all persons in the greater Cincinnati area who have an interest in or are concerned with the field of statistics.

Cincinnati Chapter of the American Statistical Association

Join us to learn about tree-based machine learning methods in this exciting half-day ASA short course!

Indiana Wesleyan University – Cincinnati Education and Conference Center

American Statistical Association Half-Day Short CourseTree-Based Machine Learning Methods for Prediction and Variable SelectionInstructors: Hemant Ishwaran, Professor, Biostatistics, University of Miami, and Min Lu, Research Assistant Professor, Biostatistics, University of MiamiLocation: Indiana Wesleyan University – Cincinnati Education and Conference Center, 9266 Schulze Drive, West Chester Township, Ohio 45069 (Parking is free)Date: September 9, 2025 (8 am - 12 pm)Course description:Tree-based machine learning methods offer several benefits in data analysis, including non-linearity, robustness, scalability and handling mixed data types. This course emphasizes practical learning with hands-on code examples and result interpretations, which is essential for understanding and applying these techniques. Based on the widely popular R package "randomForesSRC", we will present methods for computing predicted outcomes, variable importance indices and other inference estimates. In addition, we will introduce a new model-independent variable selection method, called the rule-based variable priority, and present its implementation using the R package "varPro". For all these analyses, we will cover different types of outcomes including continuous, categorical, multivariate, survival and competing risk outcomes. Utilizing real-world datasets from medicine and public health, topics in these analyses will provide hands-on code, working examples and result interpretations. We will provide additional code for visualizing model results and constructing coefficient tables for interpretation, and address scenarios such as imbalanced classes, unsupervised problems, fast implementation on big data and protection of confidential data.

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Tree-Based Machine Learning Methods for Prediction and Variable Selection

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Tree-Based Machine Learning Methods for Prediction and Variable Selection

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