The accuracy value of the confusion matrix is 92% .
In the field of machine learning, specifically the problem of statistical classification (in unsupervised learning, it is typically referred to as a matching matrix), a confusion matrix, also known as an error matrix, is a specific table structure that enables visualizing the performance of an algorithm.
The literature describes both iterations of the matrix, where each row represents instances in an actual class and each column represents instances in a predicted class. Since it is straightforward to tell whether the system is combining two classes, the name was chosen (i.e. commonly mislabeling one as another).
accuracy is given by the formula :
Accuracy(%) = (true positive + true negative)/(positive + negative)
Here TP true positive =50
and TN true negative is 42
hence accuracy = (50+42) / (1+0) %
Accuracy = 92%
Therefore the accuracy value of the confusion matrix is 92% .
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