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EP 18 | Duke ML for PMs: Model Evaluation & Business Metrics
A model can have 99% accuracy and still fail your users. In this episode, we tackle Model Evaluation from the Product Manager's perspective. We bridge the gap between technical model metrics (what engineers care about) and product/business metrics (what stakeholders care about).
Key Topics:
The Accuracy Trap: Why "Accuracy" is often a misleading metric, especially with imbalanced datasets.
The Confusion Matrix: Breaking down True/False Positives and Negatives so you can visualize exactly where your model is making mistakes.
Precision vs. Recall: The ultimate PM trade-off. Should you optimize to catch every single edge case (high recall) or ensure every alert is perfectly correct (high precision)?
System vs. Business Metrics: Balancing model performance with latency, user task success rates, and ultimate ROI.
Note: This is an AI-generated study resource created via NotebookLM based on Duke University’s ML for Product Managers curriculum and personal study notes.
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By Jack LakkapragadaA model can have 99% accuracy and still fail your users. In this episode, we tackle Model Evaluation from the Product Manager's perspective. We bridge the gap between technical model metrics (what engineers care about) and product/business metrics (what stakeholders care about).
Key Topics:
The Accuracy Trap: Why "Accuracy" is often a misleading metric, especially with imbalanced datasets.
The Confusion Matrix: Breaking down True/False Positives and Negatives so you can visualize exactly where your model is making mistakes.
Precision vs. Recall: The ultimate PM trade-off. Should you optimize to catch every single edge case (high recall) or ensure every alert is perfectly correct (high precision)?
System vs. Business Metrics: Balancing model performance with latency, user task success rates, and ultimate ROI.
Note: This is an AI-generated study resource created via NotebookLM based on Duke University’s ML for Product Managers curriculum and personal study notes.