Which approach utilizes modeling to determine patterns in unlabeled data?

Unsupervised learning is the correct approach for determining patterns in unlabeled data. This method is designed to analyze and interpret data without prior labels or classifications, allowing it to uncover hidden structures and relationships within the data itself.

In unsupervised learning, algorithms explore the data to identify clusters, associations, or outliers, enabling insights into the natural groupings present in the dataset. For example, a clustering algorithm can segment customers into groups based on purchasing behavior without having prior knowledge of the distinct classes or labels of those customers.

On the other hand, supervised learning relies on labeled data where the outcome is known, and the model is trained to predict or classify new examples based on that labeled training set. Regression analysis focuses specifically on predicting continuous outcomes rather than identifying patterns in unlabeled data. Classification analysis involves categorizing data into predefined classes, which also requires labeled input. Therefore, unsupervised learning is uniquely positioned to handle unlabeled data and extract meaningful patterns from it.