How does the Isolation Forest work?

Can you explain how the Isolation Forest algorithm works?

Sure! The Isolation Forest algorithm is used for anomaly detection. It isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of that feature.

What makes it different from other anomaly detection methods?

Unlike other methods, Isolation Forest focuses on isolating anomalies instead of profiling normal data points. This makes it efficient and effective for high-dimensional datasets.

How does it handle high-dimensional data?

It handles high-dimensional data well because it builds multiple trees, and each tree is constructed using a random subset of features, which helps in isolating anomalies effectively.

Can you give an example of its application?

Certainly! It's commonly used in fraud detection, where it can identify unusual transaction patterns that may indicate fraudulent activity.