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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #86

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscr

The correct answer is B. Configure sampled Shapley explanations on Vertex Explainable AI.. Assigns credit for the outcome to each feature, and considers different permutations of the features. This method provides a sampling approximation of exact Shapley values. shampled shapely recommended Model Type: Non-differentiable models, such as ensembles of trees and neural n

Submitted by miguelv· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions

Question

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

Options

  • ATrain local surrogate models to explain individual predictions.
  • BConfigure sampled Shapley explanations on Vertex Explainable AI.
  • CConfigure integrated gradients explanations on Vertex Explainable AI.
  • DMeasure the effect of each feature as the weight of the feature multiplied by the feature value.

How the community answered

(36 responses)
  • A
    8% (3)
  • B
    69% (25)
  • C
    19% (7)
  • D
    3% (1)

Explanation

Assigns credit for the outcome to each feature, and considers different permutations of the features. This method provides a sampling approximation of exact Shapley values. shampled shapely recommended Model Type: Non-differentiable models, such as ensembles of trees and neural networks. https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview

Topics

#Explainable AI#Feature Attribution#Vertex Explainable AI#Model Interpretability

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