CLASSIFICATION AND REGRESSION TREES
Introduction to algorithms and applications of classification and regression trees. Recursive partitioning, pruning, and cross-validation estimation of prediction error. Class priors and misclassification costs. Univariate and linear splits. Linear and kernel discriminant analysis and nearest-neighbor classification. Unbiased variable selection and importance scoring of variables. Least-squares, quantile, Poisson, logistic, and proportional hazards regression tree models. Tree ensembles. Subgroup identification of differential treatment effects. Multiple and longitudinal response variables. Missing values and multiple missing value codes. Comparisons with neural networks, support vector machines, and other methods. Bootstrap calibration and post-selection inference. Applications to business, social science, engineering, biology, medicine, and other fields.
Prerequisites & course information
Requisites: STAT 333 , 340 , graduate/professional standing, or declared in Statistics VISP Course Designation: Core GenEd - Natural Science & Wellness Breadth - Social Science or Natural Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Know the weaknesses and limitations of traditional statistical modeling methods Audience: Undergraduate 2. Understand the strengths and capabilities of the classification and regression tree approach Audience: Undergraduate 3. Learn to analyze data with missing values without missing value imputation Audience: Undergraduate 4. Learn to analyze data containing circular or periodic variables, such as angle of impact, time of day, day of week, and month of year Audience: Undergraduate 5. Learn how to build regression tree models for least squares regression, logistic regression, Poisson regression, quantile regression, and proportional hazards regression Audience: Undergraduate 6. Learn how to build prediction models for univariate, multivariate, longitudinal, and censored dependent variables Audience: Undergraduate 7. Learn to use GUIDE and R software for algorithms such as AMELIA and MICE (for missing value imputation), and RPART, MOB, random Forest (for tree and forest models) Audience: Undergraduate Learning Outcomes: 1. Know the weaknesses and limitations of traditional statistical modeling methods Audience: Undergraduate 2. Understand the strengths and capabilities of the classification and regression tree approach Audience: Undergraduate 3. Learn to analyze data with missing values without missing value imputation Audience: Undergraduate 4. Learn to analyze data containing circular or periodic variables, such as angle of impact, time of day, day of week, and month of year Audience: Undergraduate 5. Learn how to build regression tree models for least squares regression, logistic regression, Poisson regression, quantile regression, and proportional hazards regression Audience: Undergraduate 6. Learn how to build prediction models for univariate, multivariate, longitudinal, and censored dependent variables Audience: Undergraduate 7. Learn to use GUIDE and R software for algorithms such as AMELIA and MICE (for missing value imputation), and RPART, MOB, random Forest (for tree and forest models) Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027