Practice Shap (shapley Additive Explanations) (3.2) - Explainable AI (XAI) and Model Interpretability

Practice: SHAP (SHapley Additive exPlanations)

Lesson 3.2Explainable AI (XAI) and Model Interpretability

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does SHAP stand for?
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What does the acronym SHAP mean?
Question 2.
What is the main purpose of SHAP?
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Why do we use SHAP in model interpretation?
3Question 3.
What does SHAP represent?
- SHapley Additive exPlanations
- Super High Accuracy Predictions
- Simplicity in High Action Programs
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What are the first letters of those words?
4Question 4.
True or False: SHAP is solely applicable to decision trees.
- True
- False
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Think about the definition of model-agnostic.
Question 5.
Consider a model predicting customer churn based on features such as age, tenure, and service usage. Use SHAP to explain how each feature influences the prediction for a specific customer.
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Think about the individual contribution of each feature and how they add up.
Question 6.
Using SHAP, create a tutorial on interpreting feature importance in a machine learning model for credit scoring. Include examples and potential implications of trust.
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Focus on how the explanations can help users understand and trust the prediction.

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