Spring 2027

Classification and Regression Trees

Introduction to algorithms and applications of classification and regression trees, covering recursive partitioning, pruning, cross-validation, and various regression tree models.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Wei-Yin Loh

The course was considered extremely difficult by some, and the final project accounted for 60% of the total grade.

What you’ll be able to do

  • Know the weaknesses and limitations of traditional statistical modeling methods

  • Understand the strengths and capabilities of the classification and regression tree approach

  • Learn to analyze data with missing values without missing value imputation

  • Learn to analyze data containing circular or periodic variables, such as angle of impact, time of day, day of week, and month of year

  • Learn how to build regression tree models for least squares regression, logistic regression, Poisson regression, quantile regression, and proportional hazards regression

  • Learn how to build prediction models for univariate, multivariate, longitudinal, and censored dependent variables

  • 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)

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

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

  • take one
STAT 443

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

WEI-YIN LOH is recorded teaching in Fall 2022, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 750
LEC 002Classroom Instruction0 / 200

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Wei-Yin Loh

Students described Wei-Yin Loh as a captivating and approachable lecturer who kept them engaged, though opinions on the course's overall quality varied significantly.

Recent recorded grades — Fall 2022: 3.37 GPA, 65.2% A/AB (n=46 letter grades); Spring 2026: 3.67 GPA, 98.3% A/AB (n=58 letter grades).

difficulty & workload

Past student reviews · Wei-Yin Loh

The course was considered extremely difficult by some, and the final project accounted for 60% of the total grade.

Past student reviews · Wei-Yin Loh

Students found lecture materials difficult to navigate, noting hundreds of pages of unannotated code output and a lack of recordings to review missed content.

Topics

  • Recursive partitioning, pruning, and cross-validation
  • Class priors, misclassification costs, and split types
  • Discriminant analysis and nearest-neighbor classification
  • Regression tree models including least-squares, quantile, Poisson, logistic, and proportional hazards
  • Tree ensembles and subgroup identification
  • Handling missing values and multiple missing value codes
  • Comparisons with neural networks and support vector machines
  • Bootstrap calibration and post-selection inference

Skills

  • Understanding the strengths and capabilities of the classification and regression tree approach
  • Analyzing data with missing values without missing value imputation
  • Building regression tree models for least squares, logistic, Poisson, quantile, and proportional hazards regression
  • Building prediction models for univariate, multivariate, longitudinal, and censored dependent variables
  • Using GUIDE and R software for algorithms such as AMELIA, MICE, RPART, MOB, and random Forest

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
STAT 443 · Spring 2027

Classification and Regression Trees

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:932:025630",
    "course_id": "STAT 443",
    "course_uid": "course_bbb14bdd283ccb9368b888c2",
    "term_id": "1274",
    "source_course_id": "025630",
    "source_subject_id": "932",
    "title": "Classification and Regression Trees",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "52ab51aa23064147570ae56e97de2ec7b0f3dec17c7bdefe443e8140dbdd5d79",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}