https://github.com/fayzi-dev/pyod
PyOD (Python Outlier Detection)
https://github.com/fayzi-dev/pyod
cblof iforest-model knn-algorithm outlier-detection pyod
Last synced: about 1 month ago
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PyOD (Python Outlier Detection)
- Host: GitHub
- URL: https://github.com/fayzi-dev/pyod
- Owner: fayzi-dev
- Created: 2024-10-23T19:05:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-01T16:52:47.000Z (about 1 year ago)
- Last Synced: 2025-06-05T17:47:06.509Z (6 months ago)
- Topics: cblof, iforest-model, knn-algorithm, outlier-detection, pyod
- Language: Python
- Homepage: https://github.com/fayzi-dev/PyOD
- Size: 127 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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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:
1. **Wide Range of Algorithms**: PyOD includes numerous algorithms for outlier detection, such as:
- Statistical methods (e.g., Z-Score, Grubbs’ Test)
- Machine learning methods (e.g., Isolation Forest, One-Class SVM)
- Ensemble methods (e.g., Feature Bagging, Average KNN)
- Proximity-based methods (e.g., KNN, LOF - Local Outlier Factor)
2. **User-Friendly API**: The library is designed to be intuitive, enabling users to easily implement and test different algorithms without extensive coding.
3. **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.
4. **Performance Evaluation**: PyOD provides utilities for evaluating the performance of outlier detection algorithms using various metrics, such as precision, recall, and F1 score.
5. **Visualization Tools**: The library includes visualization functions to help users interpret the results of outlier detection.
6. **Support for Multidimensional Data**: PyOD is capable of handling high-dimensional datasets, which is essential for many real-world applications.
PyOD 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:
```bash
pip install pyod
```
For more information, you can visit the official [PyOD documentation](https://pyod.readthedocs.io/en/latest/).