A data mining algorithm has been applied to a transaction dataset and has classified $88$ records as fraudulent $(30$ correctly so$)$ and $952$ as nonfraudulent $(920$ correctly so$).$ Which of the following situations represents the confusion matrix for the transactions data mentioned? Explain your reasoning.

A$.$

$\backslash$table$[[$Classification Confusion Matrix$],[,,],[$Actual Class,$1,0],[1,58,920],[0,30,32]]$

B$.$ Classification Confusion Matrix Predicted Class

$\backslash$table$[[$Actual Class,$1,0],[1,32,30],[0,58,920]]$

C$.$ Classification Confusion Matrix

Predicted Class

Actual Class

$1$

$0$

$1$

$30$

$32$