Which of the following is relevant to unsupervised learning?

The relevance of hierarchical clustering to unsupervised learning lies in its fundamental purpose within this domain. Unsupervised learning focuses on analyzing and interpreting data without labeled responses, seeking to uncover hidden patterns or groupings within the data. Hierarchical clustering is a technique specifically designed for this purpose, as it organizes data points into a structure that reflects their similarity or distance from each other, effectively forming clusters.

In hierarchical clustering, the method progresses either through a bottom-up approach (agglomerative) or a top-down approach (divisive), creating dendrograms that visually represent the relationships among data points. This enables the identification of inherent structures within the data without prior information about what those structures might be, making it an invaluable tool in unsupervised learning.

Contrastingly, the other options are more closely associated with supervised learning or other statistical methodologies. Heteroscedasticity pertains to the variability of errors in regression models and is concerned with the assumption of constant variance, which is not a focus of unsupervised learning. Regression analysis itself is typically a supervised learning approach where predictions are made based on labeled data, and model fitting involves estimating parameters from such data. Therefore, hierarchical clustering stands out as the primary method relevant to unsupervised learning