We want to see how many cases the model has correctly predicted fraud if there was actual fraud, and correctly predicted no fraud if there was actually no fraud. Write some R code to generate a contingency (pivot) table comparing actual_fraud with predicted_fraud.

Calculate the proportion of claims where

the model made the correct classification of the fraud status of the claim.