I've proposed using an anomaly detection algorithm in a project.
The algorithm would consist of choosing some features we think might be indicative of anomalous examples. Then using a training set of anomalous and non-anomalous examples to fit parameters to a Gaussian distribution. We would then use these parameters to create a probability function p where p(x) < epsilon for some epsilon would indicate an anomaly.
How would I answer this challenge?
“We love this concept, but how does this differ from us just doing some statistics on a set of results?”
By just doing some statistics, I assume the challenger means detecting the anomalies manually in old data and coming up with a set of conditions that would indicate anomalies in future data.