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

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Efficiency and flexibility: Isolation Forest exhibits remarkable robustness especially in high-dimensional datasets due to its ability to remove anomalies through random splitting. Unlike traditional methods like k-means or hierarchical clustering, it does not have to Isolation Forest calculates the distance between data points also remains small, which makes it highly scalable for real-time anomaly detection tasks.Tolerance for outliers: One of Isolation Forest’s most notable strengths is its tolerance for outliers. By design, the algorithm excels at reducing anomalies by performing separations that separate repeated data points. This makes it particularly effective in cases where the anomalies are small or show distinct differences from the norm. Furthermore, since forest segmentation does not rely on distance-based methods, it is less susceptible to the effects of outliers, ensuring reliable anomaly detection performance with different data sets in various fieldsEase of implementation and interpretation: Isolation is quite straightforward to implement, due to its simple design and minimal overhead. The simplicity of the algorithm makes it easy for lack of labor more machine learning capabilities, allowing for rapid deployment in a variety of applications. Furthermore, the binary partitioning nature of Isolation Forest facilitates interpretability, as anomalies are identified based on their isolation paths within the constructed trees. This transparency enhances trust in the detection results and facilitates post-analysis interpretation for decision-making.Handling High-Dimensional Data: Isolation Forest excels in handling high-dimensional data, which poses challenges for many traditional anomaly detection techniques. By randomly selecting features for partitioning, the algorithm effectively mitigates the curse of dimensionality, maintaining robust performance even in datasets with numerous variables. This makes Isolation Forest well-suited for applications such as image processing, text mining, and sensor data analysis, where datasets often exhibit complex, multidimensional structures....

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What is Isolation Forest?

Implementation with Isolation Forest