How Isolation forest Algorithm Works?
Before jumping to the working principal of Isolation Forest algorithm, let’s discuss the two main essential concepts of it:
- Random Partitioning: In Isolation Forest, random partitioning involves selecting a random feature and then choosing a random value within the range of that feature’s values to split the data. This process is repeated recursively to create a partitioning tree, where each partition isolates a subset of the data. By randomly partitioning the data, Isolation Forest efficiently separates anomalies from normal data points, as anomalies are more likely to end up in smaller, isolated partitions.
- Isolation Path: The isolation path of a data point within an isolation tree represents the number of splits required to isolate that data point. Anomalies, being less representative of the overall data distribution, typically require fewer splits to isolate compared to normal data points. By measuring the length of isolation paths across multiple trees, Isolation Forest computes an anomaly score for each data point, enabling the identification of outliers based on their deviation from the norm.
Workings of Isolation Forest algorithm
- Random Partitioning: Isolation Forest operates by randomly selecting features and splitting data points along these features at random thresholds, creating isolation trees.
- Recursive Isolation: Each partition isolates a subset of data points, aiming to separate anomalies from normal observations by creating increasingly smaller partitions.
- Anomaly Identification: Anomalies are identified as data points requiring fewer splits to isolate, as they typically deviate further from the norm and are less likely to be randomly selected for partitioning.
- Creating Isolation Path: The isolation path of a data point within the tree is measured by the number of splits required to isolate it, serving as a measure of its anomaly score.
- Ensemble of Trees: Isolation Forest constructs multiple isolation trees independently, forming an ensemble that collectively evaluates anomalies based on their isolation paths across the trees.
- Difference score calculation: The mean separation distance across all trees is calculated for each data point, yielding an anomaly score indicating the amount of deviation from the standard.
- Classification: Predefined thresholds are used to distinguish between normal and abnormal patterns and then the Data points with anomaly scores above the threshold are flagged as anomalies.
What is Isolation Forest?
Isolation forest is a state-of-the-art anomaly detection algorithm which is very famous for its efficiency and simplicity. By removing anomalies from a dataset using binary partitioning, it quickly identifies outliers with minimal computational overhead, making it the way to go for anomalies in areas ranging from cybersecurity to finance. In this article, we are going to explore the fundamentals of Isolation Forest algorithm.
Table of Content
- What is Isolation Forest?
- How Isolation forest Algorithm Works?
- Implementation with Isolation Forest
- Advantages of Isolation Forest
- Limitations of Isolation Forest