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Anomaly Detection Using Isolation Forest: A Comprehensive Guide
Anomaly detection is a vital task in various applications like fraud detection, network security, manufacturing quality control, and predictive maintenance. One of the most efficient algorithms for anomaly detection is the Isolation Forest. This article provides an overview of the Isolation Forest, its pros and cons, use cases, and a Python example to illustrate its usage.
What is Isolation Forest?
The Isolation Forest algorithm is a tree-based ensemble method specifically designed for unsupervised anomaly detection. Unlike other algorithms, which focus on profiling normal data points, Isolation Forest aims to isolate anomalies.
It works by randomly partitioning the dataset. Since anomalies are rare and different from the normal data, fewer random partitions are needed to isolate them. In contrast, normal data points require more partitions due to their similarity with each other.
How Does Isolation Forest Work?
- Random partitioning: Isolation Forest creates multiple decision trees by recursively splitting the dataset into two parts based on random feature selection and random split points.
- Isolation of anomalies: Anomalies, being distinct, are isolated quickly and end up…
