DP-100 · Question #200
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might…
The correct answer is A. Yes. This is the correct solution. It uses TabularExplainer from the azureml.interpret package, which is the recommended wrapper for producing both global and local feature importance in Azure Machine Learning. Internally, TabularExplainer selects the most appropriate SHAP explainer…
Question
Options
- AYes
- BNo
How the community answered
(35 responses)- A89% (31)
- B11% (4)
Explanation
This is the correct solution. It uses TabularExplainer from the azureml.interpret package, which is the recommended wrapper for producing both global and local feature importance in Azure Machine Learning. Internally, TabularExplainer selects the most appropriate SHAP explainer for the given model type (for logistic regression it uses the SHAP Linear or Kernel explainer). You can then call .explain_global() to get overall feature importance across the training set and .explain_local() on a specific set of instances to get per-prediction importance values. This satisfies both stated requirements, so the answer is Yes.
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