Evaluation Metrics - Is Your AI Actually Good?

Public lesson preview

Learn why accuracy alone is misleading, and master the metrics - precision, recall, F1, ROC-AUC, BLEU, and perplexity - that truly measure AI performance.

Learning objectives

By the end of this lesson, you will understand:

• ✓The Accuracy Trap
• ✓The Confusion Matrix
• ✓Precision - "Of Everything I Flagged, How Much Was Correct?"
• ✓Recall - "Of Everything That Was Positive, How Much Did I Find?"
• ✓The Precision–Recall Trade-Off

Lesson outline

1. The Accuracy Trap
2. The Confusion Matrix
3. Precision - "Of Everything I Flagged, How Much Was Correct?"
4. Recall - "Of Everything That Was Positive, How Much Did I Find?"
5. The Precision–Recall Trade-Off
6. F1 Score - The Harmonic Mean
7. ROC Curves and AUC
8. Metrics for Text Generation
9. BLEU Score
10. Perplexity
11. A/B Testing in Production
12. When to Use Which Metric
13. Key Takeaways
14. 📚 Further Reading