Exam Professional Machine Learning Engineer topic 1 question 69 discussion

I think it's B, since we want to reduce false positives

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.

Answer is D: Since the goal is to minimize false positives (incorrectly accepting a non-compliant photo), having more examples of non-compliant photos in the training data will help the model better identify these cases. By training with more non-compliant examples, the model will learn to recognize these images more accurately, thus reducing the chance of falsely accepting them.

A is wrong because we are trying to minimise false positives, not false negatives. The question states that the model identifies compliance (rather than non-compliance) so a positive means compliant. B is correct, though one would usually say "we are trying to optimise precision", optimising F1 is the only answer that addresses this, albiet not as directly as I'd like. C and D are nonsense.

A) Minimise False Negatives

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).

Gonna go with B on this one, tricky question but since reducing false positives is the goal here only B fits that requirement

I went with 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.

Think is B since we need to optimize for percision

Minimize False positive. Hence percision. D is the closest.

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

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

I vote B

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.

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 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."