Spring 2027

Decision Trees for Multivariate Analysis

Decision Trees for Multivariate Analysis covers tree construction, pruning, error estimation, handling complex data types, selection bias, and extensions to regression models.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

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Recent recorded grades — Fall 2018: 4.00 GPA, 100.0% A/AB (n=22 letter grades); Fall 2021: 3.83 GPA, 100.0% A/AB (n=29 letter grades); Spring 2024: 4.00 GPA, 100.0% A/AB (n=18 letter grades).

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average GPA
letter grades
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Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
STAT 761

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  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Spring 2027

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Student experience

the class

Recent recorded grades — Fall 2018: 4.00 GPA, 100.0% A/AB (n=22 letter grades); Fall 2021: 3.83 GPA, 100.0% A/AB (n=29 letter grades); Spring 2024: 4.00 GPA, 100.0% A/AB (n=18 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Tree construction, split finding, pruning, and error estimation.
  • Categorical predictors, missing/censored data, prior probabilities, and misclassification costs.
  • Selection bias and comparison with other statistical and machine learning methods.
  • Extensions to piecewise linear and non-least squares regression models.

Skills

  • Constructing decision trees, finding splits, pruning, and estimating error.
  • Handling categorical predictors, missing/censored data, priors, and misclassification costs.
  • Extending methods to piecewise linear and non-least squares regression models.

Grades

Latest available · Spring 2024— not enough history to project Spring 2027.

average GPA
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letter grades
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Grade distribution · % of letter grades

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BC
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D
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Grades over time

Through Spring 2024

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Sources & history

UW–Madison

Catalog & offerings

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Dataset & model

Recorded 2026-10-14

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Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "2d47e5cd6f3ebacf0c7a20ce4d6ecffd0eaee92280315128b92bf51cb1b60d95",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}