COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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TP/TN-FN
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TP/TP-FN
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TP/TP+FN
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TP/FN+TP-TN
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Detailed explanation-1: -False negative (FN), type II error, miss, underestimation. True positive rate (TPR), recall, sensitivity (SEN), probability of detection, hit rate, power.
Detailed explanation-2: -Abbreviations: PPV, Positive predicted value; NPV, Negative predicted value; TP, True Positive; FP, False Positive; FN, False Negative; TN, True Negative.
Detailed explanation-3: -Mathematically, recall is defined as follows: Recall = T P T P + F N. Note: A model that produces no false negatives has a recall of 1.0. Let’s calculate recall for our tumor classifier: True Positives (TPs): 1.
Detailed explanation-4: -The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either precision or recall are zero.
Detailed explanation-5: -Recall and True Positive Rate (TPR) are exactly the same. So the difference is in the precision and the false positive rate. The main difference between these two types of metrics is that precision denominator contains the False positives while false positive rate denominator contains the true negatives.