Isolation Forest is an algorithm used for anomaly detection that identifies outliers in large datasets by isolating observations in a random way. It operates on the principle that anomalies are few and different, making them easier to isolate compared to normal observations. This method builds multiple decision trees to create an ensemble model, which helps in distinguishing between normal data points and anomalies effectively.
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