Answer-first summary for fast verification
Answer: Yes
The TabularExplainer in Azure Machine Learning supports both global and local feature importance explanations, making it suitable for the requirements. According to Microsoft documentation and community consensus (with high upvotes), TabularExplainer can explain both overall feature importance across the dataset (global) and for individual predictions (local). This contrasts with PFIExplainer, which only supports global explanations. Since the question specifically requires both global and local importance calculations for a logistic regression model, TabularExplainer meets the goal.
Author: LeetQuiz Editorial Team
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You train a classification model using logistic regression. You need to explain the model's predictions by calculating the importance of each feature, both as a global relative importance value and as a local importance measure for specific predictions.
You create a TabularExplainer.
Does this solution meet the goal?
A
Yes
B
No
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