Application, Communication & Impact

The value of a model lies not in its accuracy, but in the decisions it enables.

Introduction

Communicating ML results to non-technical stakeholders is as important as the modelling itself. This topic covers interpretability, visualisation, and business impact.

What You Will Learn

• Explain model predictions using SHAP and LIME
• Create compelling visualisations of ML results
• Quantify business impact and ROI of ML solutions
• Present technical findings to non-technical audiences

Assessment Connection

Section C (Impact & Conclusion) — the rubric rewards those who "anticipate objections with data evidence" (70%+).

Content

TutorialsHow-to GuidesReferenceExplanation

• SHAP Values
• LIME Explanations
• Feature Importance Visualisation
• ROI & Business Impact Analysis

• Write an Executive Summary
• Build a Stakeholder Presentation
• Anticipate & Handle Objections
• Create a Model Card

• SHAP Library Reference
• LIME Library Reference
• Presentation Quality Checklist

• Interpretability vs Accuracy
• Ethics in Machine Learning
• ML in the Workplace
• Responsible AI Principles