What is unsupervised learning?

Unsupervised learning is characterized by its focus on identifying patterns and structures in data without the use of labeled outcomes. In this form of machine learning, algorithms analyze input data to detect similarities, groupings, or trends, allowing the system to understand the data's inherent structure. This process often involves techniques such as clustering and association, where the model draws conclusions based on the underlying features of the dataset.

Given this definition, the choice that describes unsupervised learning accurately is the one that states it involves identifying patterns without the use of labels. This distinction is essential, as it contrasts with supervised learning, which relies on labeled datasets to train the model. Understanding this concept is fundamental for practitioners working with data, as it defines a significant part of the machine learning landscape, highlighting how machines can learn and draw insights from raw data autonomously.