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Question 20/123
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?
Correct Answer: A
This would allow you to identify which features contributed the most to the model's decision, which would in turn help you to better explain why the model rejected the customer's loan request.
Varying features independently could also be a useful approach, as this would help to identify specific threshold values that change the classification. However, this strategy would be more time-consuming and would not provide as much insight into the inner-workings of the model compared to using local feature importance.
Varying features independently could also be a useful approach, as this would help to identify specific threshold values that change the classification. However, this strategy would be more time-consuming and would not provide as much insight into the inner-workings of the model compared to using local feature importance.
- Question List (123q)
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- Question 19: You have recently used TensorFlow to train a classification ...
- Question 20: You are an ML engineer at a bank. You have developed a binar...
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- Question 24: You manage a team of data scientists who use a cloud-based b...
- Question 25: You are working on a binary classification ML algorithm that...
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- Question 44: You are a lead ML engineer at a retail company. You want to ...
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- Question 50: You have trained an XGBoost model that you plan to deploy on...
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- Question 52: You are collaborating on a model prototype with your team. Y...
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- Question 69: You recently designed and built a custom neural network that...
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