Outlier Detection
时间: 2024-06-01 10:10:23 浏览: 15
Outlier detection, also known as anomaly detection, is the process of identifying data points or observations that deviate significantly from the expected or normal pattern of a dataset. These outliers may be caused by errors in data collection or processing, or they may represent unusual or unexpected occurrences in the data.
The detection of outliers is important in many fields, including finance, medicine, and environmental monitoring, as they can indicate potential problems or opportunities for further investigation. There are many statistical and machine learning techniques that can be used to detect outliers, including clustering, regression analysis, and neural networks. These techniques can help to identify outliers and provide insights into the underlying patterns and relationships in a dataset.
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