Topic 1 Question 25
You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to AI Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the AI Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?
Increase the recall.
Decrease the recall.
Increase the number of false positives.
Decrease the number of false negatives.
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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)
👍 26Paul_Dirac2021/06/24Precision = 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.
👍 13Danny20212021/09/08Answer 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 .
👍 3Bemnet2021/12/10
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