nerdexam
Microsoft

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…

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Question

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 have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You train a classification model by using a logistic regression algorithm. You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions. You need to create an explainer that you can use to retrieve the required global and local feature importance values. Solution: Create a PFIExplainer. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(35 responses)
  • A
    89% (31)
  • B
    11% (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.

Topics

#Explainable AI#Feature Importance#Model Interpretability#Azure Machine Learning

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