Module 3 Narration#
Opening#
Open with the professional setting: an analytics team choosing a predictive model for an operational decision. Ask students what decision is being made, who is affected, and what evidence would be persuasive to a skeptical reviewer.
Middle#
Move through the module in four passes:
Define Linear and tree-based baselines in the context of Machine Learning & Predictive Modeling.
Walk through the lab as a proxy-data exercise, emphasizing what it can and cannot show.
Compare a baseline with an AI-enabled or more sophisticated alternative.
Translate the result into stakeholder language: recommendation, risk, mitigation, and next evidence.
Closing#
Close by returning to the module artifact: predictive modeling report with baseline comparison, validation evidence, and model card focused on linear and tree-based baselines: Compare logistic regression or linear regression with a tree model.. Students should leave knowing exactly what artifact they are producing and how it will be judged.