Running classification on data
Unsupervised anomaly detection is useful when looking for abnormal or unexpected behavior in a dataset to guide investigation and analysis. It can unearth silent faults, unexpected usage patterns, resource abuse, or malicious user activity. This is just one class of use cases enabled by machine learning.
It is common to have historical data where, with post analysis, it is rather easy to label or tag this data with a meaningful value. For example, if you have access to service usage data for your subscription-based online application along with a record of canceled subscriptions, you could tag snapshots of the usage activity with a label indicating whether the customer churned.
Consider a different example where an IT team has access to web application logs where, with post analysis, given the request payloads are different to normal requests originating from the application, they can label events that indicate malicious activity, such as password...
