Outlier detection in all its glory

Spoooooky.

At first glance, the problems of detecting credit card fraud, COVID in wastewater, trashed highway stops, meth “smurfs” buying sudafed (yes, that’s the actual term), concussed athletes returning to the field, and speech patterns associated with brain and/or speech disorders appear to have nothing in common. But all of them are potential applications of outlier detection.

In any work modeling data, finding and dealing with outliers is a necessary part of the job. Here, we define outliers as points or individual observations that differ significantly from the rest of the dataset. They are sometimes referred to as anomalies. The inclusion…

Clair McLafferty

Clair is a data scientist, student, writer, and sometimes bartender who loves to explore the innumerable intersections of data and everyday life.

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