Practice Bootstrapping (12.5.A) - Model Evaluation and Validation

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9 questions

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6 questions · about 4 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is bootstrapping?
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Think about how we can simulate data sampling.
Question 2.
Why do we use bootstrapping?
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Consider situations with limited data.
3Question 3.
What is the purpose of bootstrapping?
- To improve dataset size
- To estimate confidence intervals
- To classify data
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Consider what bootstrapping achieves statistically.
4Question 4.
Bootstrapping uses sampling with replacement. True or False?
- True
- False
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Reflect on our keyword 'replacement'.
Question 5.
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.
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Focus on how you derive the mean from the bootstrap samples.
Question 6.
Critically discuss how bootstrapping could lead to incorrect conclusions in a model evaluation if the data sample is not representative of the population.
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Consider the implications of having a skewed dataset.

5 more questions in this section. Enroll free to practise them all.