ExIFFI and EIF+ Documentation
This is the official documentation of the implementation used in the "ExIFFI and EIF+: Interpretability and Enhanced Generalizability to Extend the Extended Isolation Forest" paper. The paper introduces Extended Isolation Forest Feature Importance (ExIFFI), a novel interpretation algorithm designed for the Extended Isolation Forest (EIF) anomaly detection model but it also works for all the Isolation Forest based AD models. ExIFFI aims to provide explanations for predictions made by EIF by computing global and local feature importance scores. Additionally, an enhanced variant of EIF, named EIF+, is proposed to improve generalization performance. The evaluation involves comprehensive experiments on synthetic and real-world datasets to assess anomaly detection performance and the effectiveness of ExIFFI for interpretation. The code used to perform the experiments is also included in this documentation to provide reproducibility.