Practice Intrinsic (2.3) - Explainable AI (XAI) and Model Interpretability

Practice: Intrinsic

Lesson 2.3Explainable AI (XAI) and Model Interpretability

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10 questions

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6 sample questions

Question 1.
Define intrinsic interpretability in your own words.
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Think about how a simple model can be directly understood.
Question 2.
Give an example of an interpretable model.
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Consider models that don’t require extra tools to explain.
3Question 3.
What is intrinsic interpretability?
- Models that provide understandable results without tools
- Models that are too complex to understand
- Models that require additional tools for explanations
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Consider models that are simpler and clearer.
4Question 4.
True or False: In linear regression, negative coefficients indicate a negative relationship between predictors and outcomes.
- True
- False
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Think about the direction of dependencies.
Question 5.
Imagine you are a data scientist tasked with selecting a model for a healthcare prediction system where understanding the model's decision-making is critical. Which model would you advocate and why?
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Consider what models allow users to follow a clear decision path.
Question 6.
Compare how you would communicate the results of a decision made by a black box model versus a linear regression model to a non-technical audience.
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Think about the clarity of information.

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