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…

Option A is correct because using local feature importance from the predictions is the best way to

provide the reasons that contributed to the model’s decision for a specific customer’s loan

request. Local feature importance is a measure of how much each feature affects the prediction for a

given instance, relative to the average prediction for the dataset1. AutoML Tables provides local

feature importance values for each prediction, which can be accessed using the Vertex AI SDK for

Python or the Cloud Console2. By using local feature importance, you can explain why the model

rejected the loan request based on the customer’s data.

Option B is incorrect because using the correlation with target values in the data summary page is

not a good way to provide the reasons that contributed to the model’s decision for a specific

customer’s loan request. The correlation with target values is a measure of how much each feature is

linearly related to the target variable for the entire dataset, not for a single instance3. The data

summary page in AutoML Tables shows the correlation with target values for each feature, as well as

other statistics such as mean, standard deviation, and histogram4. However, these statistics are not

useful for explaining the model’s decision for a specific customer, as they do not account for the

interactions between features or the non-linearity of the model.

Option C is incorrect because using the feature importance percentages in the model evaluation

page is not a good way to provide the reasons that contributed to the model’s decision for a specific

customer’s loan request. The feature importance percentages are a measure of how much each

feature affects the overall accuracy of the model for the entire dataset, not for a single instance5. The

model evaluation page in AutoML Tables shows the feature importance percentages for each feature,

as well as other metrics such as precision, recall, and confusion matrix. However, these metrics are

not useful for explaining the model’s decision for a specific customer, as they do not reflect the

individual contribution of each feature for a given prediction.

Option D is incorrect because varying features independently to identify the threshold per feature

that changes the classification is not a feasible way to provide the reasons that contributed to the

model’s decision for a specific customer’s loan request. This method involves changing the value of

one feature at a time, while keeping the other features constant, and observing how the prediction

changes. However, this method is not practical, as it requires making multiple prediction requests,

and may not capture the interactions between features or the non-linearity of the model.

Reference:

Local feature importance

Getting local feature importance values

Correlation with target values

Data summary page

Feature importance percentages

[Model evaluation page]

[Varying features independently]