What is a common characteristic of supervised learning?

In supervised learning, a key characteristic is the clear distinction between input and output. This approach involves training a model on a labeled dataset, where each training example is paired with a corresponding label or target output. This relationship allows the model to learn how to predict output values based on given input features effectively. The presence of labeled data is crucial because it provides the necessary context for the model to understand the mapping between inputs and their desired outputs, enabling it to make accurate predictions on unseen data.

The focus on input-output pairing distinguishes supervised learning from unsupervised learning, where no labeled outputs are provided. This makes it essential for tasks where a specific prediction is required, whether it be a continuous variable for regression or a discrete class label for classification.