Anomaly Detection

Anomaly detection is the process of identifying data points or patterns that deviate from the expected behavior of a given dataset. In data science and artificial intelligence, anomaly detection is used to identify unusual or rare events, errors, or outliers that may indicate a problem or an opportunity for further investigation. Anomaly detection can be performed using various techniques such as statistical methods, machine learning algorithms, and deep learning models. These techniques can be supervised, unsupervised, or semi-supervised, depending on the availability of labeled data. Anomaly detection has various applications in different domains such as fraud detection, intrusion detection, fault detection, predictive maintenance, and quality control.
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