sklearn.svm.OneClassSVM is a module in the scikit-learn library that implements the One-Class Support Vector Machines (SVM) algorithm. This algorithm is used for training an unsupervised model that can detect anomalies or outliers in a dataset. OneClassSVM can learn a representation of the normal instances in the dataset and identify any instances that deviate from this learned representation. It is particularly useful in situations where only one class of data is available for training. The module provides various parameters to customize the algorithm's behavior, such as kernel type, gamma value, and nu value. Overall, sklearn.svm.OneClassSVM is a powerful tool for detecting anomalies or outliers in a given dataset.
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