Multivariate Outliers and Mahalanobis Distance in Python. In this article, we will be discussing the distance metric called Mahalanobis Distance for detecting outliers in multivariable data.
Detecting outliers in multivariate data can often be one of the challenges of the data preprocessing phase. There are various distance metrics, scores, and techniques to detect outliers. Euclidean distance is one of the most known distance metrics to identify outliers based on their distance to the center point. There is also a Z-Score to define outliers for a single numeric variable. In some cases, clustering algorithms can be also preferred. All these methods consider outliers from different perspectives. The outliers are found based on one method may not be found by the others as outliers. Therefore, these methods and metrics should be chosen by considering the distribution of the variables. However, this brings out the needs of different metrics too. In this article, we will be discussing the distance metric called Mahalanobis Distance for detecting outliers in multivariable data.
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