Topic 1 Question 69
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?
Use AutoML to optimize the model’s recall in order to minimize false negatives.
Use AutoML to optimize the model’s F1 score in order to balance the accuracy of false positives and false negatives.
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.
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.
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I think it's B, since we want to reduce false positives
👍 11LearnSodas2022/12/10- 正解だと思う選択肢: A
False Negative means it is False that the picture does not follow the requirements -> Accepted. I.e. the model predicts that a non-compliant picture (N) is not against the requirements (F), and so accepted. Tricky one.
FP would say that it is false that the picture follows the requirements, and so it rejects a valid image.
True Positive + False Negative = Actual Positive = Accepted
👍 4taxberg2023/01/31 - 正解だと思う選択肢: B
B. Option B, "Use AutoML to optimize the model’s F1 score in order to balance the accuracy of false positives and false negatives", is the best approach to build an ML model that can predict whether a user's submitted profile photo meets the requirements while ensuring that the application does not falsely accept a non-compliant picture.
👍 3lucaluca19822023/04/25
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