In the digital age, safeguarding business operations from fraud is more complex than meets the eye. It's not just about identifying deceit; it's about protecting brand integrity and customer trust. This demands a shift in focus from traditional metrics like Recall and Precision to a more nuanced approach. This article explores the advanced metrics that help enterprises safeguard their operational integrity and brand reputation.
With an equal balance of classes, our error matrix will look like this: Among all fraud, we will find 98% of objects. And we will also mark 98% of all legitimate events correctly. Now, let's calculate our error matrix for these algorithms for class balances. This can be implemented as follows: def random_algorithm: TN=count_0 * 0.5 FP=count_0 * 0.5 TP=count_1 * 0.5 FN=count_1 * 0.5 return TN, FP, TP, FN def perfect_algorithm: TN=count_0 * 0.98 FP=count_0 * 0.02 TP=count_1 * 0.
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