Isolation forests are an anomaly detection technique used in machine learning that identifies outliers by isolating observations in a dataset. This method works by constructing a forest of random trees, where each observation is split recursively until it is isolated. The concept connects to broader themes in machine learning, emphasizing the importance of unsupervised learning and model performance in detecting anomalies across various applications.
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