PyOD: A Python Toolbox for Scalable Outlier Detection
This is an incremental tool for practitioners and researchers in data science and machine learning to access multiple outlier detection methods efficiently.
PyOD is a Python toolbox that tackles the challenge of scalable outlier detection on multivariate data by providing a unified API for various algorithms, including ensembles and neural networks, with a focus on robustness and scalability.
PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization are emphasized as core components in the toolbox's development. PyOD is compatible with both Python 2 and 3 and can be installed through Python Package Index (PyPI) or https://github.com/yzhao062/pyod.