IsolationForestOutliers
Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots.
Purpose
The IsolationForestOutliers test is designed to identify anomalies or outliers in the model’s dataset using the isolation forest algorithm. This algorithm assumes that anomalous data points can be isolated more quickly due to their distinctive properties. By creating isolation trees and identifying instances with shorter average path lengths, the test is able to pick out data points that differ from the majority.
Test Mechanism
The test uses the isolation forest algorithm, which builds an ensemble of isolation trees by randomly selecting features and splitting the data based on random thresholds. It isolates anomalies rather than focusing on normal data points. For each pair of variables, a scatter plot is generated which distinguishes the identified outliers from the inliers. The results of the test can be visualized using these scatter plots, illustrating the distinction between outliers and inliers.
Signs of High Risk
- The presence of high contamination, indicating a large number of anomalies
- Inability to detect clusters of anomalies that are close in the feature space
- Misclassifying normal instances as anomalies
- Failure to detect actual anomalies
Strengths
- Ability to handle large, high-dimensional datasets
- Efficiency in isolating anomalies instead of normal instances
- Insensitivity to the underlying distribution of data
- Ability to recognize anomalies even when they are not separated from the main data cloud through identifying distinctive properties
- Visually presents the test results for better understanding and interpretability
Limitations
- Difficult to detect anomalies that are close to each other or prevalent in datasets
- Dependency on the contamination parameter which may need fine-tuning to be effective
- Potential failure in detecting collective anomalies if they behave similarly to normal data
- Potential lack of precision in identifying which features contribute most to the anomalous behavior