Description: One-class SVM, or One-Class Support Vector Machine, is a specific approach within machine learning primarily used for anomaly detection. Unlike traditional classification models that require multiple classes for training, one-class SVM is trained using only examples from a particular class, making it ideal for situations where positive class data is abundant but negative class data is scarce or nonexistent. This method creates a model that attempts to learn the distribution of the known class and, from there, identifies any data that significantly deviates from this distribution as an anomaly. Key features of this approach include its ability to handle imbalanced data and its robustness against noise in the data. Additionally, one-class SVM uses a margin that maximizes the distance between data points of the positive class and the decision boundary, allowing for better generalization in anomaly detection. Its relevance lies in its application across various fields, such as fraud detection, system monitoring, and fault identification in diverse processes, where identifying unusual behaviors is crucial for safety and efficiency.
