Isolation Forest

Isolation Forest is an algorithm used to detect unusual data points, or anomalies, within a dataset. It works by randomly partitioning data points with decision trees. Normal points tend to require many partitions to isolate, while anomalies, being rare and different, are quickly isolated with fewer splits. By measuring how easily each point is separated, the algorithm identifies those that stand out as potential anomalies. It's efficient and effective for large datasets, making it a popular choice for fraud detection, network security, and quality control.