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.