Practice Bootstrapping - 12.5.A | 12. Model Evaluation and Validation | Data Science Advance
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Bootstrapping

12.5.A - Bootstrapping

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is bootstrapping?

💡 Hint: Think about how we can simulate data sampling.

Question 2 Easy

Why do we use bootstrapping?

💡 Hint: Consider situations with limited data.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the purpose of bootstrapping?

To improve dataset size
To estimate confidence intervals
To classify data

💡 Hint: Consider what bootstrapping achieves statistically.

Question 2

Bootstrapping uses sampling with replacement. True or False?

True
False

💡 Hint: Reflect on our keyword 'replacement'.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You are given a dataset with 100 observations. Describe how you would employ bootstrapping to calculate confidence intervals for the mean of a chosen numerical feature.

💡 Hint: Focus on how you derive the mean from the bootstrap samples.

Challenge 2 Hard

Critically discuss how bootstrapping could lead to incorrect conclusions in a model evaluation if the data sample is not representative of the population.

💡 Hint: Consider the implications of having a skewed dataset.

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Reference links

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