Anomaly Detection

PyOD (Python Outlier Detection)

PyOD is a comprehensive and scalable Python library for detecting outliers/anomalies in multivariate data. It includes more than 40 detection algorithms including deep learning approaches (AutoEncoder, VAE), proximity-based methods (LOF, CBLOF), linear models (PCA, OCSVM), and ensemble methods (IForest, LOCI). Widely used in research and production for fraud detection, intrusion detection, medical anomaly detection, and data quality monitoring.

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API entry from apis.yml

name: PyOD (Python Outlier Detection)
description: PyOD is a comprehensive and scalable Python library for detecting outliers/anomalies in multivariate
  data. It includes more than 40 detection algorithms including deep learning approaches (AutoEncoder,
  VAE), proximity-based methods (LOF, CBLOF), linear models (PCA, OCSVM), and ensemble methods (IForest,
  LOCI). Widely used in research and production for fraud detection, intrusion detection, medical anomaly
  detection, and data quality monitoring.
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
humanURL: https://pyod.readthedocs.io/
baseURL: https://pypi.org/project/pyod/
tags:
- Anomaly Detection
- Data Science
- Machine-Learning
- Open-Source
- Outlier Detection
- Python
tags_raw:
- Anomaly Detection
- Data Science
- Machine Learning
- Open Source
- Outlier Detection
- Python
properties:
- type: Documentation
  url: https://pyod.readthedocs.io/en/latest/
- type: APIReference
  url: https://pyod.readthedocs.io/en/latest/pyod.html
- type: GitHubRepository
  url: https://github.com/yzhao062/pyod
- type: SDKs
  url: https://pypi.org/project/pyod/
contact:
- FN: PyOD Maintainers
  url: https://github.com/yzhao062/pyod/issues