What Are PCA Loadings And How To Effectively Use Biplots?

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A practical guide for getting the most out of Principal Component Analysis.

Principal Component Analysis is one of the most well-known techniques for data analysis. The most common usage is dimensionality reduction and 2D plotting. Less familiar are the loadings and the interpretation of the biplot. This is a crucial concept for any data scientist who utilizes PCA, as it enables you to have a deeper understanding of how original variables contribute to each principal component, allowing for more informed insights, feature interpretation, and storytelling through visualizations like the biplot. In this article, I will build up the information on: 1. how to explain the variance in your data by interpreting the loadings, 2. how to select the most informative features, 3. how to create beautiful and insightful plots, and finally, 4. how to detect outliers. I will begin with a theoretical background, and then continue with practical hands-on examples.