STSCI 4725

Data Analysis with Tree-Based Methods

Course Description

Course information provided by the 2025-2026 Catalog.

Statistical learning methods based on decision trees are flexible and intuitive solutions to classification and regression problems, which nevertheless are straightforward to interpret. This course will introduce the classification and regression tree model (CART), as well as more advanced tree-based approaches, including, as time permits, gradient-boosted trees, bagged trees, random forest, and Bayesian additive regression trees. We will use the R Programming Language to apply these methods to real data and compare them to other regression and classification methods. We will discuss advantages and disadvantages of tree-based methods, and cover presentation, interpretation, and visualization of results.

Prerequisites An introductory statistics course and a course that covers R programming.

Last 4 Terms Offered (None)

Learning Outcomes

• Identify the core features of tree-based classification and regression methods.
• Apply tree-based methods to real data using R packages.
• Analyze the results of tree-based methods, and effectively communicate those results to others.

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