Uses of Outliers
- Anomaly Detection: Identifying unusual patterns in data.
- Quality Control: Monitoring for defects or irregularities.
- Financial Analysis: Detecting fraudulent activities or unusual transactions.
- Predictive Modeling: Improving model accuracy by handling outliers appropriately.
Outlier
Outliers stand for data points that are indicative of a much higher variability than other observations in a given dataset. This can result in skewing statistical studies and wrong conclusions after all the variables are not adequately identified and handled. Identifications of outliers are very relevant for the financial sector, healthcare industry and decision-making processes that depend on data analysis.
In this article, we will learn in detail about outlier, its definition, examples, types, how to find outlier, their uses and how they are different of inliers.
Contact Us