What is the approach characterized by supervised learning?

The approach characterized by supervised learning is the method that involves learning from labeled examples provided by a "teacher." In supervised learning, algorithms are trained on a dataset that includes both input data and corresponding output labels. This relationship allows the model to recognize patterns and make predictions on new, unseen data by understanding the specific connection between the inputs and the expected outputs.

Supervised learning is fundamental in scenarios where the desired outcome is known and can be used to effectively teach the algorithm. Common applications include classification tasks, like identifying spam emails, and regression tasks, such as predicting house prices based on various features.

The other approaches mentioned involve different methodologies that do not fit within the supervised learning framework. For example, using large amounts of unlabeled data corresponds more closely with unsupervised learning, where the algorithm must identify patterns without predefined labels. Implementing random sampling techniques and utilizing real-time feedback are also practices relevant to machine learning but do not define supervised learning.