an unsupervised anomaly detection algorithm that works by isolating anomalies from normal instances in a dataset based on their unique statistical properties
Isolation Forest was used to detect fraudulent credit card transactions in a financial dataset. The algorithm was able to identify a subset of transactions that were significantly more likely to be fraudulent than the rest of the data.