Contributions to this project included the implementation of two state-of-the-art anomaly detection algorithms, namely the Isolation Forest algorithm and the Local Outlier Factor algorithm. The first one has been merged on scikit- learn development version, while the second one still needs some work.
This contribution also includes participation to the scikit-learn maintenance and pull requests review. This project was a real success as it offers to scikit-learn the tool to better address academic and industrial data challenges related to anomaly and novelty detection (predictive maintenance, device monitoring, etc.).
