Which statement best describes supervised learning?

Supervised learning is a type of machine learning that specifically relies on labeled data to train models. In this approach, the algorithm is provided with a dataset containing input-output pairs, where the inputs are the features (or independent variables) and the outputs are the corresponding labels (or dependent variables). The primary goal is for the model to learn the mapping from inputs to outputs during the training phase, which allows it to make accurate predictions on unseen data.

This reliance on labeled data is what distinguishes supervised learning from unsupervised learning, where models work with unlabelled data to find patterns and relationships without explicit output labels. Supervised learning is commonly used across various applications, from classification tasks to regression tasks, and is not limited to any specific area, including image processing.

The other statements fail to adequately define supervised learning or present inaccuracies about its requirements and applications.