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CLASSIFICATION AND REGRESSION TREES

STAT 443
Course Description

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.

Prerequisties

STAT 333 , STAT 340 , graduate/professional standing, or declared in Statistics VISP

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.37

No change from Historical

Completion Rate
97.83%

No change from Historical

A Rate
30.43%

No change from Historical

Class Size
46

No change from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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