Beginning Anomaly Detection Using

 Anomaly  Detection

With a better understanding of the different types of anomalies you can encounter, you 

can now proceed to start creating models to detect them. Before you do that, there are a 

couple approaches you can take, although you are not limited to just these methods.

Recall the reasoning for labeling the swan as an anomaly. One of the reasons was 

that since all the swans you saw thus far were white, the black swan was the anomaly. 

Another reason was that since the probability of a swan being black was very low, it was 

an anomaly since you didn’t expect that outcome.

The anomaly detection models you will explore in this book will follow these 

approaches by either training on normal data to classify anomalies, or classifying 

anomalies by their probabilities if they are below a certain threshold. However, in one 

of the classes of models that you choose, the anomalies and normal data points will 

both labeled as such, so you will basically be told what swans are normal and what 

swans are anomalies.

Chapter 1   What Is anomaly DeteCtIon?

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Finally, let’s explore anomaly detection.