Practice Overfitting and Underfitting - 12.6 | 12. Evaluation Methodologies of AI Models | CBSE 12 AI (Artificial Intelligence)
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Overfitting and Underfitting

12.6 - Overfitting and Underfitting

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What happens when a model overfits?

💡 Hint: Think about how well a student does on practice tests versus the actual exam.

Question 2 Easy

What is the main issue with underfitting?

💡 Hint: Consider how a student might miss key concepts in their studies.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is overfitting?

A model that performs well on seen data only
A model that performs equally well on new data
A model that is too simple

💡 Hint: Consider how well the model can predict on new data versus training data.

Question 2

True or False: Underfitting refers to a model that has high variance.

True
False

💡 Hint: Remember the definitions of variance and bias.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Consider a dataset where the relationship between variables is quadratic, but you use a linear regression model. What issues arise, and how can this be resolved?

💡 Hint: What additional terms could capture the relationship between your variables?

Challenge 2 Hard

You have data that is very noisy, and a model is trained to fit every point exactly. Explain the likely outcomes and the adjustments you could make to improve generalization.

💡 Hint: What techniques can you think of to penalize complexity?

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