What is the objective of supervised learning in machine learning?

The objective of supervised learning in machine learning is to use labeled data to train algorithms to predict outcomes. In this approach, each training example is paired with an output label, which guides the learning process. The model learns from the input-output pairs, aiming to make accurate predictions on unseen data based on the patterns it recognizes in the training data.

The essence of supervised learning is that it is "supervised" by the labels provided during training. As the model is exposed to more examples, it refines its understanding of the relationships between input features and the corresponding outputs, improving its ability to generalize and make predictions. This is a fundamental principle of how predictive models operate in supervised learning contexts, such as classification or regression tasks.