What is anomaly detection

Anomaly detection in Machine Learning is the process of identifying data points that are unusual or unexpected compared to the majority of the data. It is used to detect outliers, which are data points that are significantly different from the rest of the data. Anomaly detection is used in a variety of applications, such as fraud detection, healthcare, and cybersecurity. It can be used to identify unusual patterns in data that may indicate a problem or potential security risk. Anomaly detection algorithms use statistical techniques to identify data points that are significantly different from the rest of the data. These algorithms can be used to detect outliers, which can be used to identify potential problems or security risks. Anomaly detection can also be used to identify changes in data over time, which can be used to monitor for changes in patterns or trends.