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 = 0.8, Recall = 0.4
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Precision = 0.4, Recall = 0.8
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Precision = 0.7, Recall = 0.7
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None of the above
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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: -Fraud Detection Machine Learning Algorithms Using Logistic Regression: Logistic Regression is a supervised learning technique that is used when the decision is categorical. It means that the result will be either ‘fraud’ or ‘non-fraud’ if a transaction occurs.
Detailed explanation-4: -The commonly used machine learning algorithms include LR, Naïve Bayes and KNN. The KNN has an accuracy rate of 97.69% when it comes to the detection of fraudulent transactions in Credit card [13].