Society of Actuaries (SOA) PA Practice Exam 2025 – 400 Free Practice Questions to Pass the Exam

Unsupervised learning refers to a category of machine learning algorithms that are used when the data does not have labeled responses. In unsupervised learning, the algorithm tries to learn the structure and patterns from the data without any prior training on labeled output.

K-Means clustering is a quintessential example of unsupervised learning. This algorithm identifies clusters within an unlabeled dataset by partitioning the data into a specified number of clusters based on feature similarities. It calculates the centroids of the clusters and assigns data points to the nearest centroid, iteratively refining these clusters until convergence. The lack of output labels in this process exemplifies the unsupervised nature of K-Means clustering.

In contrast, Generalized Linear Models and Decision Trees are both examples of supervised learning, where the models are trained on a labeled dataset with target outputs. Regularization techniques are used to prevent overfitting in both supervised learning algorithms and are not a standalone learning method. Thus, they do not fit the criteria of unsupervised learning. Therefore, K-Means clustering stands out as the correct answer in the context of unsupervised learning.