Practice Pre-processing Strategies (data-level Interventions) (1.3.1) - Advanced ML Topics & Ethical Considerations (Weeks 14)
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Pre-processing Strategies (Data-Level Interventions)

Practice - Pre-processing Strategies (Data-Level Interventions)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is bias in the context of machine learning?

💡 Hint: Think about how data might reflect past inequalities.

Question 2 Easy

Define re-sampling in one sentence.

💡 Hint: How do we balance contributions from different groups?

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does re-sampling aim to achieve in machine learning?

To enhance data quality.
To balance demographic representation.
To increase model complexity.

💡 Hint: Consider how unbalanced data affects model predictions.

Question 2

True or False: Re-weighing assigns equal importance to all samples in a dataset.

True
False

💡 Hint: What does it mean to prioritize certain samples?

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Create a questionnaire to assess bias in a dataset used for hiring. What aspects will you analyze?

💡 Hint: Think about what categories of bias could impact hiring.

Challenge 2 Hard

Design a scenario where re-sampling could fail and lead to worse outcomes. Describe how to avoid this in practice.

💡 Hint: Consider negative outcomes from one-sided approaches.

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Reference links

Supplementary resources to enhance your learning experience.