isolationsforest

The Isolation Forest is a machine learning algorithm designed for anomaly detection. It works by randomly selecting a feature and then randomly selecting a split value between the minimum and maximum values of that feature. This process is repeated recursively to create a tree. The key idea is that anomalies are "few and different," meaning they are likely to be isolated closer to the root of the tree. The algorithm builds an ensemble of such trees, and the anomaly score for a data point is based on its average path length across all trees. Shorter path lengths indicate a higher likelihood of being an anomaly. This method is efficient and effective, particularly for large datasets, as it doesn't require any distance calculations or density estimations, which can be computationally expensive. It is also robust to irrelevant features, as they do not contribute to the isolation of data points. The algorithm is unsupervised, meaning it does not require labeled data, making it suitable for scenarios where anomalies are rare and difficult to define.