Outlier
An Outlier is a data point that differs substantially from the general pattern or distribution of other observations in a dataset. Outliers can result from errors, unusual events, or genuine variation in the underlying data.
What is an Outlier?
An outlier is an observation that falls unusually far from other values in a dataset. It can be identified using statistical methods, visualization, or machine learning techniques. Whether an outlier should be removed, transformed, or retained depends on its cause and relevance to the analysis.
Why is Outlier Important?
Outliers can significantly affect statistical analysis and machine learning models, particularly algorithms that are sensitive to extreme values. Identifying them helps teams investigate data quality issues, understand unusual behavior, and determine whether special handling is necessary.
Common use cases
Outlier detection is commonly used in fraud detection, anomaly detection, data cleaning, financial analysis, sensor monitoring, healthcare, and machine learning preprocessing.