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Introduction

Instruction and application
In Progress

Introduction

Imagine presenting a model with 92% accuracy to leadership and being asked a simple but powerful question: How does that 92% translate into business value?

This unit reframes evaluation metrics as business communication tools, not just technical scores. You will revisit familiar measures like accuracy, precision, recall, and mean squared error through a more applied lens.

By the end, you will be better equipped to choose metrics that match the real goal of the model and explain those choices to stakeholders.

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Why does this unit matter?

Metrics shape how models are trusted, adopted, and judged. The wrong metric can hide risk, while the right one helps connect technical performance to operational and financial outcomes.

This is especially important in high-stakes domains like healthcare, finance, sustainability, and operations where model trade-offs carry real business consequences.

Learning objectives​

By the end of this unit, you will be able to:

  • Analyze performance metrics and their applicability to different ML models and business scenarios.
  • Evaluate how performance metrics align with business requirements in machine learning applications.
  • Develop strategies for refining performance metric selection as business needs and ML models evolve.
  • Implement custom performance metrics that reflect model effectiveness for business needs.
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Action item: Pause and think

Think about a time when a metric looked impressive on paper but failed to capture what mattered in the real world. What was missing from the measurement?

Questions & reflections
Have you ever used a performance metric that looked good on paper but did not reflect how the model performed in the real world? What happened?
Your reflection here...
Why is it important to align performance metrics with business needs rather than technical accuracy alone?
Your reflection here...