Isolation Forest

Isolation Forest is an efficient and scalable tree-based method for anomaly detection. Its core assumption is that anomalous points can be isolated with fewer splits than normal observations. This makes it especially useful in large-scale and unlabeled environments. However, the data distribution, feature structure, and business-specific definition of anomaly must still be evaluated carefully.