University of Michigan - Ann Arbor

Isolation forests isolate outliers found using anomaly detection. This is adverse to other techniques that typically use data profiling (which are statistical methods used to describe the data set as a whole).


Isolation  Forest

Isolation Forest Code Example

- Finance: finding anomalies of fraudulent purchases, high bank deposit regulation
- Networking: outside threats to intrude on networks


Examples of when to use Isolation Forests


**Pros:**
- Does not require scaling of values.
- Effective when no given distribution fits the data, as statistical analysis cannot be used for outliers.
- Requires few parameters.

**Cons:**
- Often restricted to specific libraries like scikit-learn in practical implementations.
- Visualization of results is more complex than traditional outlier detection methods.
- Training time can be extremely long if implemented incorrectly.

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