Practice Post-processing Strategies (Output-Level Interventions) - 1.3.3 | Module 7: Advanced ML Topics & Ethical Considerations (Weeks 14) | Machine Learning
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1.3.3 - Post-processing Strategies (Output-Level Interventions)

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a post-processing strategy?

πŸ’‘ Hint: Think about the timing of the intervention.

Question 2

Easy

What does threshold adjustment involve?

πŸ’‘ Hint: It relates to modifying thresholds.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

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

  • Increase model complexity
  • Enhance fairness
  • Reduce model performance

πŸ’‘ 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

πŸ’‘ Hint: Consider when the model is sure or unsure.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

πŸ’‘ Hint: Consider data-driven thresholds based on evidence.

Question 2

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?

πŸ’‘ Hint: Think about balancing speed and thoroughness.

Challenge and get performance evaluation