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Question 91 - Professional Machine Learning Engineer discussion

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You need to build an ML model for a social media application to predict whether a user's submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

A.
Use AutoML to optimize the model's recall in order to minimize false negatives.
Answers
A.
Use AutoML to optimize the model's recall in order to minimize false negatives.
B.
Use AutoML to optimize the model's F1 score in order to balance the accuracy of false positives and false negatives.
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B.
Use AutoML to optimize the model's F1 score in order to balance the accuracy of false positives and false negatives.
C.
Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.
Answers
C.
Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.
D.
Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.
Answers
D.
Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.
Suggested answer: A

Explanation:

Recall is the ratio of true positives to the sum of true positives and false negatives. It measures how well the model can identify all the relevant cases. In this scenario, the relevant cases are the pictures that do not meet the profile photo requirements. Therefore, minimizing false negatives means minimizing the cases where the model incorrectly predicts that a non-compliant picture meets the requirements. By using AutoML to optimize the model's recall, the model will be more likely to reject a non-compliant picture and inform the user accordingly.Reference:

[AutoML Vision] is a service that allows you to train custom ML models for image classification and object detection tasks. You can use AutoML to optimize your model for different metrics, such as recall, precision, or F1 score.

[Recall] is one of the evaluation metrics for ML models. It is defined as TP / (TP + FN), where TP is the number of true positives and FN is the number of false negatives. Recall measures how well the model can identify all the relevant cases. A high recall means that the model has a low rate of false negatives.

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asked 18/09/2024
Leon Duke
37 questions
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