Week 9 · 45-60 min
Model Evaluation
Week Summary
A quick preview of the main ideas in this week before you begin the slides.
- Why Evaluate Models?
- Accuracy for Classification
- The Confusion Matrix
- Precision and Recall (Conceptual Intro)
Glossary
Review these key terms before starting this week. They are kept simple so students can learn the new vocabulary before moving into the slides.
- Why Evaluate Models?
- Why Evaluate Models?
- Accuracy for Classification
- Accuracy for Classification
- The Confusion Matrix
- The Confusion Matrix
Why Evaluate Models?
Progress
1 / 8
Measure and interpret how well a model performs.
Read one card at a time. Focus on the main idea first, then look at the visual on the right to help it stick.
A model only matters if its performance is checked.
Evaluation prevents blind trust in predictions.
Testing data gives a fair performance check.
Different tasks need different metrics.
Evaluation is required, not optional, in ML.
Remember
Machine learning without evaluation is guesswork, not trustworthy analysis.
Why Evaluate Models?
Slide 1 of 8
Use arrows or dots to move through the lesson
Practice
- Write 2-3 sentences about what you learned this week.
Coming Next Week: Next: Overfitting and Underfitting