What is supervised learning?

Supervised learning is defined as a machine learning technique that involves using labeled data for training. In this context, labeled data refers to a dataset that includes both input features and the corresponding output labels or targets. The primary goal of supervised learning is to create a model that can accurately predict the output when given new, unseen input data.

During the training process, the algorithm learns the relationship between the input features and the output labels by analyzing the provided data. Once the model has been trained, it can be tested on new data to evaluate its performance and make predictions. This method is commonly used in various applications, including classification tasks (where the output is a discrete label) and regression tasks (where the output is continuous).

In contrast, other methods mentioned focus on different aspects of data or training techniques. For example, the options that refer to unstructured data or unlabeled data do not align with the principles of supervised learning, which specifically relies on labeled datasets to guide the training process. By emphasizing the requirement for labeled data, the correct answer highlights the essence of supervised learning and its foundational role in many machine learning applications.