The `sklearn.ensemble.IsolationForest` is a class in the Python library `scikit-learn` (sklearn) that provides an implementation of the Isolation Forest algorithm for anomaly detection. Anomaly detection refers to the task of identifying observations that significantly deviate from the normal behavior of a dataset.
The Isolation Forest algorithm works by constructing isolation trees from the data, which are binary trees built using random splits on feature values. The isolation trees are constructed in such a way that anomalies are more likely to be isolated in the leaves of the trees, while normal data points are more likely to be located closer to the root.
By using an ensemble of isolation trees and averaging their results, the Isolation Forest algorithm can effectively identify anomalies in datasets, even in the presence of complex structures or high-dimensional data. It provides a simple and efficient approach for anomaly detection, making it suitable for various applications such as fraud detection, network intrusion detection, and outlier detection in general.
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