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Answer: Use AutoML to optimize the model’s recall in order to minimize false negatives.
The correct answer is A. The problem explicitly states that the application should not falsely accept a non-compliant picture. In this context, a non-compliant picture is considered a positive instance. False acceptance of non-compliant pictures corresponds to false negatives, where the model incorrectly labels a non-compliant picture as compliant. To reduce this, we need to minimize false negatives. Optimizing for recall helps in minimizing false negatives, as recall measures the proportion of actual positives (non-compliant pictures) that are correctly identified by the model. Therefore, using AutoML to optimize the model’s recall in order to minimize false negatives is the best approach.
Author: LeetQuiz Editorial Team
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You are tasked with developing a machine learning (ML) model for a social media application. The goal of the model is to predict whether a user's submitted profile photo meets the predefined requirements set by the application. Upon analysis, the application will inform the user if the picture meets these requirements. Ensuring that the model does not falsely accept a non-compliant picture is crucial for maintaining the quality and standards of the profile photos. 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.
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.
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.
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