Classification is best described as which type of learning technique?

Classification is best described as a supervised learning technique where models are trained to assign labels to data based on features of that data. In a classification task, the model learns from a labeled dataset—where each data point is associated with a category or class. For instance, in an email filtering application, emails might be classified as "spam" or "not spam" based on various features, such as the sender address, specific keywords, and other attributes.

The key characteristic of this approach is that it relies on prior knowledge of labels, which guides the model during training. As the model processes the training data, it identifies patterns that differentiate the various classes. Once trained, the model can then predict the label of new, unseen data based on what it has learned.

In contrast, regression techniques focus on predicting continuous numerical values rather than categorical labels. Uncovering hidden patterns generally relates to unsupervised learning methods, where the data lacks labels, and the goal is to discover inherent structures without predefined categories. Analyzing unstructured data typically involves techniques suited for processing text, images, or other non-tabular inputs, further distinguishing it from classification tasks that specifically involve labeled datasets.