Outlier detection

Outlier Detection is a method of detecting unusual or unexpected observations within a dataset. It involves identifying data points that lie outside of normal behavior. Outliers are typically identified through using statistical tests such as the box plot, scatterplot, or the mean absolute deviation. Outlier detection can be used to identify fraud, find missing data, or to detect any unusual activity in a given dataset.

Outliers can be caused by various factors such as data entry errors, measurement errors, sampling errors, or by the presence of rare occurrences. Outliers can affect the accuracy and validity of a data set, and therefore, outlier detection is important for obtaining a more precise analysis result.

Outlier detection is also known as anomaly detection, outlier analysis, or anomaly analysis. It is often used to detect rare events in computer security, such as hacking attempts or malicious intent. It can also be used to detect rare items in a dataset which can be used to develop new products or services.

Outlier detection is a powerful tool used by individuals and organizations to analyze datasets for any possible outliers. It is an important part of data analysis, and can be used to make better decisions and draw more accurate conclusions.