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.

oversampling the negative class to avoid falsely labelling them as compliant

Choice between A & B. A if +ve class is non-compliant pics, B if +ve class is compliant pics, As per query, +ve class is compliant pics - "to predict whether a user’s submitted profile photo meets the requirements". Though I feel the person who framed the question really wanted A to be the choice, seems like question is wrongly framed - Selected B

D: In this case, a false positive means accepting a non-compliant picture. You want to minimize these. By providing more examples of non-compliant pictures, you train the model to be more sensitive to identifying them and less likely to make this type of error.

1. Emphasizes the minority class (non-compliant pictures), ensuring the model better differentiates non-compliant images. 2. Balances the dataset to prevent the model from favouring compliant images disproportionately. 3. Provides flexibility to fine-tune the model for this specific use case.

It's B. You are optimizing for false positives, Not false negatives(Recall)

D - minimizing false positives

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.