Practice Post-processing Strategies (output-level Interventions) (1.3.3) - Advanced ML Topics & Ethical Considerations (Weeks 14)
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Post-processing Strategies (Output-Level Interventions)

Practice - Post-processing Strategies (Output-Level Interventions)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is a post-processing strategy?

💡 Hint: Think about the timing of the intervention.

Question 2 Easy

What does threshold adjustment involve?

💡 Hint: It relates to modifying thresholds.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What do post-processing strategies in AI typically aim to achieve?

Increase model complexity
Enhance fairness
Reduce model performance

💡 Hint: Think about the definition of post-processing.

Question 2

True or False: Reject option classification allows models to make predictions with complete confidence.

True
False

💡 Hint: Consider when the model is sure or unsure.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You are implementing a credit scoring AI system that needs to minimize bias against minority groups. Describe how you might implement threshold adjustment and what considerations you need to ensure it’s effective.

💡 Hint: Consider data-driven thresholds based on evidence.

Challenge 2 Hard

In a healthcare system using an AI for diagnosing diseases, what are the potential implications of using reject option classification, and how could you ensure it improves trust without delaying treatment?

💡 Hint: Think about balancing speed and thoroughness.

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Reference links

Supplementary resources to enhance your learning experience.