What technique is employed to reduce dimensionality in data sets?

Principal component analysis (PCA) is a statistical technique used to reduce the dimensionality of datasets while preserving as much variance as possible. It transforms the original variables into a new set of uncorrelated variables called principal components, which are ordered by their variance. The first principal component captures the most variance in the data, and subsequent components capture progressively less variance. This is particularly useful in situations where datasets have many variables, as it can help simplify models, reduce computation time, and mitigate issues related to multicollinearity.

In contrast, hierarchical clustering and K-means clustering are techniques aimed at grouping data points into clusters based on their similarity, rather than reducing dimensionality. Although random forests are a robust ensemble learning method for classification and regression tasks, they also do not specifically focus on dimensionality reduction. Therefore, principal component analysis stands out as the appropriate technique for reducing dimensionality in this context.