Week 10 · 45-60 min
Overfitting and Underfitting
Week Summary
A quick preview of the main ideas in this week before you begin the slides.
- What Is Overfitting?
- What Is Underfitting?
- The Balance: Model Complexity
- Signs of Overfitting
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.
- What Is Overfitting?
- What Is Overfitting?
- What Is Underfitting?
- What Is Underfitting?
- The Balance: Model Complexity
- The Balance: Model Complexity
What Is Overfitting?
Progress
1 / 8
Understand two common ML pitfalls and how to recognize them.
Read one card at a time. Focus on the main idea first, then look at the visual on the right to help it stick.
An overfit model learns the training data too closely, including noise.
It performs very well on training data but poorly on new data.
This is like memorizing answers instead of understanding concepts.
It reduces real-world usefulness.
Recognizing overfitting is a key ML skill.
Remember
Overfitting means a model learned the training examples too specifically to generalize well.
What Is Overfitting?
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: Ethics and Bias in AI