Isolation Forest is a popular unsupervised machine learning algorithm for detecting anomalies (outliers) within datasets. Anomaly detection is a crucial part of any machine learning and data science workflow. Erroneous values that are not identified early on can result in inaccurate predictions from machine learning models, and therefore impact the interpretation of those results.

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