What is supervised learning?

Supervised learning refers to a category of machine learning where a model is trained using labeled data. In this context, labeled data consists of input-output pairs where the input is the data used for training, and the output is the correct answer or label associated with that input. During the training process, the model learns to identify patterns and relationships in the data that correlate the input features to the output labels. This enables the model to make predictions on unseen data by applying the learned patterns.

Because the focus in supervised learning is on a clearly defined output based on the provided labeled training data, it is a guided process. The model's performance can be evaluated using metrics derived from its predictions compared to the actual outcomes, allowing practitioners to refine and improve the model.

In contrast, options that suggest training without data, relying solely on unsupervised data, or transforming data into supervised formats do not align with the definition of supervised learning, as they either misrepresent the method of data usage or highlight techniques unrelated to the fundamental concept of supervised learning.