Uncertainty Quantification for ML Models

Intermediate

Learn to quantify and decompose uncertainty in model predictions. Master bootstrap methods, confidence intervals, and the critical distinction between epistemic and aleatoric uncertainty.

75 min Lab: notebook 5 objectives 4 evidence types
Open Interactive Lab
75
Minutes
5
Objectives
4
Evidence Types
4
Success Criteria

Case Narrative

Learning Objectives

1
Distinguish between epistemic and aleatoric uncertainty
2
Apply bootstrap methods for variance estimation
3
Construct and interpret confidence intervals
4
Decompose total uncertainty into meaningful components
5
Make informed decisions under uncertainty

Required Evidence

Uncertainty Classification Not collected yet
Bootstrap Analysis Not collected yet
Interval Construction Not collected yet
Decomposition Evaluation Not collected yet

Case Details

Difficulty
Intermediate
Duration
75 min
Lab Type
notebook
Slug
uncertainty-quantification

Prerequisites

No prerequisites - open to all

Success Criteria

Bootstrap Applied Required
Confidence Intervals Constructed Required
Decomposition Completed Required
Uncertainty Types Identified Required

Tags