August 2019
Intermediate to advanced
342 pages
9h 35m
English
Content preview from Hands-On Artificial Intelligence for Cybersecurity
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Evaluating a detector's performance with ROC
We have previously encountered the ROC curve and AUC measure (Chapter 5, Network Anomaly Detection with AI, and Chapter 7, Fraud Prevention with Cloud AI Solutions) to evaluate and compare the performance of different classifiers.
Now let's explore the topic in a more systematic way, introducing the confusion matrix associated with all the possible results returned by a fraud-detection classifier, comparing the predicted values with the real values:
We can then calculate the following values (listed with their interpretation) based on the previous confusion matrix:
- Sensitivity = Recall = Hit rate ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.