Exam Professional Machine Learning Engineer topic 1 question 25 discussion

Decreasing FN increases recall (D). So D and A are the same. Increasing FP decreases precision (C). Answer: B ("improving precision typically reduces recall and vice versa", https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)

Precision = TruePositives / (TruePositives + FalsePositives) Recall = TruePositives / (TruePositives + FalseNegatives) A. Increase recall -> will decrease precision B. Decrease recall -> will increase precision C. Increase the false positives -> will decrease precision D. Decrease the false negatives -> will increase recall, reduce precision The correct answer is B.

B) Decrease Recall (increases precision)

To increase precision, you have to decrese recall, increse true positives, increse false negatives and decrease false positives

Went with B

Option B is the best approach because decreasing the threshold will increase the precision by reducing the number of false positives.

A , C , D they are the same. So I go with B , it is threshold adjustment from 0.5 +-

ans: B. A: should decrease even more the precission. C: will decrease precision D: will increase recall (precision would be the same)

Precision and recall are negatively correlated, when one goes up the other goes down and vice-versa; to increase precidion we need to decrease recall, therefore answer B. (To be more complete, answer C and D are wrong because they both would increase recall, according to the recall formula)

Correct answer is "C"

Answer is D If the dataset does not change, TP + FN is constant. FN goes down then TP goes up. Hence Precision = TP / TP + FP goes up.

precision and recall have negative proportion , so to increase precision reduce recall

It's B. C,D is basically ruining your model.

Answer: D Because of Precision should respond the answer how many retrieved items are relevant? In the relation of False Negative / true positives an optima precision need a high number of true positives. If your model is precision is lower than your business requirement is because the model has a high number of false negatives. Check it in: https://en.wikipedia.org/wiki/Precision_and_recall

definitely B

I think this should be C. The reason is, for one to increase precision, the classification threshold for whether the car is there or not should be kept low. That way, even when the model is not very confident (say only 60% confident), it will say, yes, car is there. What this does is it will crease the times the model says car is present, driving up precision (when it says car is there, car is really there). The consequence of this is, False positives will increase too, reducing recall. So C is my choice. Choices A and B are not really right, as precision and recall are after-effects, not something you will control ahead.

Answer is B . 100% sure . The only way to affect precision and recall is by adjusting threshold. FN and FP go in opposite direction so C & D are the same. A increasing recall decreases precision .