Practice Threshold Adjustment (optimized For Fairness) (1.3.3.1) - Advanced ML Topics & Ethical Considerations (Weeks 14)
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Threshold Adjustment (Optimized for Fairness)

Practice - Threshold Adjustment (Optimized for Fairness)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define threshold adjustment.

💡 Hint: Think about why different groups might need different criteria.

Question 2 Easy

What is equal opportunity in the context of machine learning?

💡 Hint: Consider what 'equal' means in terms of outcomes.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main purpose of threshold adjustment?

To increase model accuracy
To ensure fairness across demographic groups
To simplify model complexity

💡 Hint: Consider the ethical implications of not addressing fairness.

Question 2

True or False: Threshold adjustment allows the same threshold to be used for all demographic groups.

True
False

💡 Hint: Think about varying needs in society.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have developed a predictive model for loan approvals. After a review, you find that it systematically denies applicants from minority groups at a higher rate. Propose a detailed plan using threshold adjustment to rectify this bias.

💡 Hint: Think about integrating data analysis with ethical principles.

Challenge 2 Hard

In a healthcare setting, a model predicts patient recovery based on various demographics. Create a strategy for adjusting thresholds to ensure equitable healthcare outcomes.

💡 Hint: Consider the health disparities when thinking about threshold settings.

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Reference links

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