Society of Actuaries (SOA) PA Practice Exam 2025 - Free Actuarial Practice Questions and Study Guide

Principal Component Analysis (PCA) is a statistical technique primarily used for transforming, extracting, and selecting features from high-dimensional data sets.

Feature transformation refers to the conversion of data from one format to another, and this is a core function of PCA as it reduces dimensionality while maintaining variance. Feature extraction involves creating new features through combinations of the original features, which is exactly what PCA does by finding principal components that represent the underlying structure of the data. Feature selection involves choosing a subset of relevant features from the original dataset. While PCA can help in identifying which features carry the most information, it transforms the features rather than simply selecting them from the original set.

On the other hand, feature engineering is a broader process that includes creating new features based on domain knowledge, which isn’t directly a result of performing PCA alone. Therefore, it is not considered a main use of PCA in the same sense as the other processes.