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Anomaly Detection in Machine Learning Using Python
- Anomaly detection is used to identify outliers, data that is outside the bounds of expectation and demonstrate behavior that is out of the norm.
- Using machine learning for anomaly detection has been a game changer. With machine learning algorithms, more complex data can be analyzed all at once.
- There are generally two main types of anomaly detection: outlier detection and novelty detection.
- In outlier detection, the approach is to use unsupervised machine learning algorithms to pick out undetected anomalies in the training data.
- Novelty detection is sometimes referred to as semi-supervised anomaly detection.
- OneClassSVM and Isolation Forest methods can be used for detecting anomalies using Python.
- OneClassSVM uses support vector machine (SVM) technology for fitting data and making decision boundaries.
- Isolation Forest is an ensemble-based method that creates many decision trees by randomly selecting parting features and values.
- By using PyCharm for machine learning on a Jupyter project, you can easily organize, analyze and visualize your data and graphs for anomaly detection.
- This blog post demonstrates how to implement OneClassSVM and Isolation Forest methods and use STI decomposition to detect anomalies in time series data.
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