{"id":21540161,"url":"https://github.com/fayzi-dev/pyod","last_synced_at":"2025-10-11T05:08:03.063Z","repository":{"id":259295487,"uuid":"877505324","full_name":"fayzi-dev/PyOD","owner":"fayzi-dev","description":"PyOD (Python Outlier Detection) ","archived":false,"fork":false,"pushed_at":"2024-11-01T16:52:47.000Z","size":130,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-06-05T17:47:06.509Z","etag":null,"topics":["cblof","iforest-model","knn-algorithm","outlier-detection","pyod"],"latest_commit_sha":null,"homepage":"https://github.com/fayzi-dev/PyOD","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fayzi-dev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-10-23T19:05:40.000Z","updated_at":"2025-05-21T00:50:52.000Z","dependencies_parsed_at":"2024-10-24T06:29:11.770Z","dependency_job_id":"a9ac3f11-3cbf-43a5-9282-6978c3d8450e","html_url":"https://github.com/fayzi-dev/PyOD","commit_stats":null,"previous_names":["fayzi-dev/pyod"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/fayzi-dev/PyOD","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fayzi-dev%2FPyOD","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fayzi-dev%2FPyOD/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fayzi-dev%2FPyOD/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fayzi-dev%2FPyOD/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fayzi-dev","download_url":"https://codeload.github.com/fayzi-dev/PyOD/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fayzi-dev%2FPyOD/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279006333,"owners_count":26084083,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-11T02:00:06.511Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cblof","iforest-model","knn-algorithm","outlier-detection","pyod"],"created_at":"2024-11-24T04:17:35.234Z","updated_at":"2025-10-11T05:08:03.045Z","avatar_url":"https://github.com/fayzi-dev.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"PyOD (Python Outlier Detection) is an open-source Python library specifically designed for detecting outliers in multivariate data. It provides a wide variety of algorithms, making it easy to apply different outlier detection techniques to datasets. Here are some key features of PyOD:\n\n1. **Wide Range of Algorithms**: PyOD includes numerous algorithms for outlier detection, such as:\n   - Statistical methods (e.g., Z-Score, Grubbs’ Test)\n   - Machine learning methods (e.g., Isolation Forest, One-Class SVM)\n   - Ensemble methods (e.g., Feature Bagging, Average KNN)\n   - Proximity-based methods (e.g., KNN, LOF - Local Outlier Factor)\n\n2. **User-Friendly API**: The library is designed to be intuitive, enabling users to easily implement and test different algorithms without extensive coding.\n\n3. **Integration with Other Libraries**: PyOD works well with other popular data science libraries like NumPy, pandas, and scikit-learn, allowing for seamless integration into existing workflows.\n\n4. **Performance Evaluation**: PyOD provides utilities for evaluating the performance of outlier detection algorithms using various metrics, such as precision, recall, and F1 score.\n\n5. **Visualization Tools**: The library includes visualization functions to help users interpret the results of outlier detection.\n\n6. **Support for Multidimensional Data**: PyOD is capable of handling high-dimensional datasets, which is essential for many real-world applications.\n\nPyOD is useful in various domains such as fraud detection, network security, fault detection, and data cleaning, where identifying outliers is critical. You can install it via pip:\n\n```bash\npip install pyod\n```\n\nFor more information, you can visit the official [PyOD documentation](https://pyod.readthedocs.io/en/latest/).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffayzi-dev%2Fpyod","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffayzi-dev%2Fpyod","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffayzi-dev%2Fpyod/lists"}