Anomaly detection with Isolation Forest

Anomaly detection is the identification of events in a dataset that do not conform to the expected pattern. In applications, these events may be of critical importance. For instance, they may be occurrences of a network intrusion or of fraud. We will utilize Isolation Forest to detect such anomalies. Isolation Forest relies on the observation that it is easy to isolate an outlier, while more difficult to describe a normal data point.