COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Precision = 0.9, Recall = 0.9
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Precision = 0.9, Recall = 0.6
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Precision = 0.6, Recall = 0.9
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Precision = 0.6, Recall = 0.6
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Detailed explanation-1: -Precision is the number of true positives divided by the sum of the true positives and false positives. It describes how good a model is at predicting the positive class [3]. Recall is the ratio of the number of true positives divided by the sum of the true positives and the false negatives.
Detailed explanation-2: -If you want to maximize recall, set the threshold below 0.5 i.e., somewhere around 0.2. For example, greater than 0.3 is an apple, 0.1 is not an apple. This will increase the recall of the system. For precision, the threshold can be set to a much higher value, such as 0.6 or 0.7.
Detailed explanation-3: -We use precision when we want the prediction of 1 to be as correct as possible and we use recall when we want our model to spot as many real 1 as possible.