Practice Interpretability Tools (qualitative Insights) (1.2.4) - Advanced ML Topics & Ethical Considerations (Weeks 14)
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Interpretability Tools (Qualitative Insights)

Practice - Interpretability Tools (Qualitative Insights)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does LIME stand for?

💡 Hint: Think about the features of LIME.

Question 2 Easy

What is the main purpose of SHAP?

💡 Hint: Think about the role of game theory in SHAP.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main goal of interpretability in AI?

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

💡 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

💡 Hint: Consider what model-agnostic means.

Get performance evaluation

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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

💡 Hint: Consider how altering features might change a model's output.

Challenge 2 Hard

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

💡 Hint: Think about the consequences in sectors like healthcare or criminal justice.

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Reference links

Supplementary resources to enhance your learning experience.