PyOD: A Python Toolbox for Scalable Outlier Detection

About

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.

Yue Zhao, Zain Nasrullah, Zheng Li · 2019

Related benchmarks

Task	Dataset	Result
Anomaly Detection	WBC	ROCAUC0.8372	151
Tabular Anomaly Detection	pima	AUC ROC0.5662	86
Tabular Anomaly Detection	BreastW	AUC-ROC0.9609	83
Tabular Anomaly Detection	Wine	AUC-ROC0.8611	72
Tabular Anomaly Detection	pendigits	AUC-ROC80.33	72
Tabular Anomaly Detection	ionosphere	AUC-ROC70.48	66
Anomaly Detection	Satimage 2	AUC—	64
Anomaly Detection	satellite	AUC—	62
Tabular Anomaly Detection	Optdigits	AUC-ROC0.7765	55
Outlier Detection	BreastW	AUC-PR0.9644	55