MACHINE LEARNING
INTRODUCTION
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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 a good measure to determine when the cost of false positives is high. E.g.-email spam detection. Recall-When there is a high cost associated with false negatives. E.g.-fraud detection or sick patient detection.
Detailed explanation-2: -Here, recall is a better measure than precision. If you were to optimize recommendations on YouTube, false negatives are less important since only a small subset of recommendations is shown anyways. Most importantly, false positives (bad recommendations) should be avoided.
Detailed explanation-3: -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.