Consider the following confusion matrix for a data set with 600 out of 11,100 instances positive: In this case, Precision = 50%, Recall = 83%, Specificity = 95%, and Accuracy = 95%. Select the…

The correct answer is B. Precision is low, which means the classifier is predicting positives poorly. In this case, Precision = 50%, Recall = 83%, Specificity = 95%: and Accuracy = 95%. In this case, Precision is low, which means the classifier is predicting positives poorly. However, the three other measures seem to suggest that this is a good classifier. This just goes to…

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Question

Consider the following confusion matrix for a data set with 600 out of 11,100 instances positive:

In this case, Precision = 50%, Recall = 83%, Specificity = 95%, and Accuracy = 95%. Select the correct statement

Exhibit

Options

APrecision is low, which means the classifier is predicting positives best
BPrecision is low, which means the classifier is predicting positives poorly
Cproblem domain has a major impact on the measures that should be used to evaluate a classifier
D1 and 3
E2 and 3

How the community answered

(32 responses)

A
3% (1)
B
72% (23)
C
16% (5)
D
6% (2)
E
3% (1)

Explanation

In this case, Precision = 50%, Recall = 83%, Specificity = 95%: and Accuracy = 95%. In this case, Precision is low, which means the classifier is predicting positives poorly. However, the three other measures seem to suggest that this is a good classifier. This just goes to show that the problem domain has a major impact on the measures that should be used to evaluate a classifier within it, and that looking at the 4 simple cases presented is not sufficient.

Topics

#confusion matrix#precision#recall#classifier evaluation

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