Practice Interpretability Tools (Qualitative Insights) - 1.2.4 | Module 7: Advanced ML Topics & Ethical Considerations (Weeks 14) | Machine Learning
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1.2.4 - Interpretability Tools (Qualitative Insights)

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does LIME stand for?

πŸ’‘ Hint: Think about the features of LIME.

Question 2

Easy

What is the main purpose of SHAP?

πŸ’‘ Hint: Think about the role of game theory in SHAP.

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 is the main goal of interpretability in AI?

  • To improve accuracy
  • To explain decision-making processes
  • To reduce data size

πŸ’‘ Hint: Think about why designers of AI would want transparency.

Question 2

True or False: SHAP provides a model-agnostic explanation for all predictions.

  • True
  • False

πŸ’‘ Hint: Consider what model-agnostic means.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

How would you apply LIME to understand a prediction made by a credit scoring model? Detail the steps.

πŸ’‘ Hint: Consider how altering features might change a model's output.

Question 2

Discuss the implications of not using interpretability tools like LIME and SHAP in high-stakes domains.

πŸ’‘ Hint: Think about the consequences in sectors like healthcare or criminal justice.

Challenge and get performance evaluation