A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it falls and causes catastrophic damage to the…

Precision is a performance metric used to evaluate the accuracy of positive predictions in a

classification model. It is defined by the formula:

Precision=TPTP+FP×100%\text{Precision} = \frac{TP}{TP + FP} \times 100\%Precision=TP+FPTP​×100%

Where:

TP (True Positives) = Number of correctly predicted positive cases

FP (False Positives) = Number of incorrectly predicted positive cases

The confusion matrix provided in the question would typically list these values. Based on ISTQB's

guidelines for calculating precision, selecting the correct number of true positives and false positives

from the given data should yield 94.2% as the precision​.

Reference from ISTQB Certified Tester AI Testing Study Guide:

Section 5.1 - Confusion Matrix and ML Functional Performance Metrics explains the calculation of

precision using the confusion matrix​.