Practice Cross-validation And Model Selection (1.11) - Learning Theory & Generalization
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Cross-Validation and Model Selection

Practice - Cross-Validation and Model Selection

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is cross-validation?

💡 Hint: Think about why we need to evaluate models differently.

Question 2 Easy

Describe K-Fold cross-validation.

💡 Hint: How many times are we training with K-Fold?

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary purpose of cross-validation?

To prevent overfitting
To increase model complexity
To train faster

💡 Hint: Consider why we need to estimate model performance better.

Question 2

True or False: Leave-One-Out cross-validation is always preferable to K-Fold cross-validation.

True
False

💡 Hint: Consider the trade-offs of computation vs. training accuracy.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset of 10,000 samples with an imbalanced class distribution of 90% to 10%, how would you set up a stratified K-Fold cross-validation?

💡 Hint: Think about the ratio of classes in each fold.

Challenge 2 Hard

Analyze the potential impact of using plain K-Fold cross-validation versus stratified K-Fold on a highly imbalanced dataset.

💡 Hint: Consider how the class ratios affect predictive modeling.

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Reference links

Supplementary resources to enhance your learning experience.