For the given confusion matrix, what is the recall and precision of the model?

For the given confusion matrix, what is the recall and precision of the model?

A . Recall = 0.92 Precision = 0.84
B . Recall = 0.84 Precision = 0.8
C . Recall = 0.92 Precision = 0.8
D . Recall = 0.8 Precision = 0.92

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Answer: C

Explanation:

Recall and precision are two metrics that can be used to evaluate the performance of a classification model. Recall is the ratio of true positives to the total number of actual positives, which measures how well the model can identify all the relevant cases. Precision is the ratio of true positives to the total number of predicted positives, which measures how accurate the model is when it makes a positive prediction.

Based on the confusion matrix in the image, we can calculate the recall and precision as follows:

Recall = TP / (TP + FN) = 12 / (12 + 1) = 0.92

Precision = TP / (TP + FP) = 12 / (12 + 3) = 0.8

Where TP is the number of true positives, FN is the number of false negatives, and FP is the number of false positives. Therefore, the recall and precision of the model are 0.92 and 0.8, respectively.