Society of Actuaries (SOA) PA Practice Exam

Principal Component Analysis (PCA) is primarily aimed at reducing the dimensionality of a dataset while preserving as much of the variation in the data as possible. This technique achieves that by transforming the original variables into a new set of uncorrelated variables called principal components. These components are ordered such that the first few retain most of the variation present in all of the original variables.

The essence of PCA lies in its ability to simplify complex datasets by condensing them into fewer dimensions, which can be particularly useful in exploratory data analysis and for improving the performance of machine learning algorithms. By focusing on the dimensions that capture the most variance, PCA helps to alleviate the "curse of dimensionality", making models more efficient and interpretable.

In contrast, the other options do not capture the primary function of PCA. For instance, while PCA can aid in data visualization, enhancing 3D plots is not its main purpose. Similarly, PCA does not classify data points but rather transforms them into a new coordinate system; classification is typically handled by other algorithms. Finally, PCA does not increase the number of features; its goal is to reduce dimensionality instead.