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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #99
You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or r
The correct answer is A. Use local feature importance from the predictions.. To access local feature importance in AutoML Tables, you can use the "Explain" feature, which shows the contribution of each feature to the prediction for a specific example. This will help you identify the most important features that contributed to the loan request being reject
Submitted by zhang_li· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions
Question
You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer's loan request has been rejected by your model, and the bank's risks department is asking you to provide the reasons that contributed to the model's decision. What should you do?
Options
- AUse local feature importance from the predictions.
- BUse the correlation with target values in the data summary page.
- CUse the feature importance percentages in the model evaluation page.
- DVary features independently to identify the threshold per feature that changes the classification.
How the community answered
(49 responses)- A73% (36)
- B16% (8)
- C4% (2)
- D6% (3)
Explanation
To access local feature importance in AutoML Tables, you can use the "Explain" feature, which shows the contribution of each feature to the prediction for a specific example. This will help you identify the most important features that contributed to the loan request being rejected.
Topics
#Explainable AI (XAI)#Local Feature Importance#Model Interpretability#AutoML Tables Explanations
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