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?
To ensure that the application does not falsely accept a non-compliant picture, it is essential to optimize the recall of the model. Recall measures the proportion of actual positives (in this case, non-compliant photos) that are correctly identified. By maximizing recall, the model aims to minimize false negatives, meaning it will correctly identify as many non-compliant pictures as possible, thereby reducing the chances of falsely accepting non-compliant pictures.
I think it's B, since we want to reduce false positives
B yes, A is incorrect as minimize false negatives does not help
a non-compliant profile image = positive false negatives = didn't alert the non-compliant profile image so the objective is to minimize false nagatives
The answer is A. The negative event is usually labeled as positive (e.g., fraud detection, customer default prediction, and here non-compliant picture identification). The question explicitly says, "ensure that the application does not falsely accept a non-compliant picture." So we should avoid falsely labeling a non-compliant image as compliant (negative). It is never mentioned in the question that false positives are also a concern. So, recall is better than F1-score for this problem.
A. Let me explain why. You may have 3 times more examples of images, however, the total number of images can be small, which lead to poor model performace, so C and D are not the for definite answer. The target is the detection of abnormal photo, so falsely accept a non-compliant picture is false negative. So A.
B. A non-compliant picture is the positive and not the negative. What the question is asking is to decrease the number of false positives ("falsely labeled as non compliant"), which is achieved through optimizing for precision and not recall. Since C and D sound a bit overkill, I would go for the one that prioritizes false positives which is B.
I went with A.
Optimize recall can help lowering the false negative cases
B should be correct. It covers not only the recall but also the precision
I was thinking B but after reading the comments I think it should be A. I was thinking a non-compliant profile image = negative but actually it should be the positive case we do want to flag out. So minimising false negative fits the requirement "ensure that the application does not falsely accept a non-compliant picture."
A. Let me explain why. You may have 3 times more examples of images, however, the total number of images can be small, which lead to poor model performace, so C and D are not the for definite answer. The target is the detection of abnormal photo, so falsely accept a non-compliant picture is false negative. So A.
I vote B
We should optimize for precision to minimize false positives, so optimizing for recall should be incorrect. F1 Score will balance both precision and recall. Both B and C might not necessarily meet the goal
Optimising for false positives is the goal here which should have been precision. Since precision is not available in options, the next best is F1 score which is harmonic mean of precision and recall. Although it wont fully satisfy the false positives it atleast wont skew towards recall which is more false positives that deviates from the goal. Hence B
Minimize False positive. Hence percision. D is the closest.
Think is B since we need to optimize for percision
Gonna go with B on this one, tricky question but since reducing false positives is the goal here only B fits that requirement
a non-compliant profile image = positive false negatives = didn't alert the non-compliant profile image so the objective is to minimize false nagatives
D. Cost of Misclassification: In this scenario, falsely accepting a non-compliant picture (false positive) is more critical than rejecting a compliant picture (false negative). A user with a non-compliant picture could violate the platform's terms or negatively impact the user experience. Training Data Imbalance: Social media applications might receive many compliant pictures and far fewer non-compliant ones. A standard training dataset might be imbalanced, with the model learning more from the majority class (compliant pictures).
A) Minimise False Negatives