What is supervised learning in the context of artificial intelligence?

Supervised learning is fundamentally about teaching machines to make decisions based on a set of labeled input-output pairs. This type of learning involves providing a model with a dataset that includes both the input data and the corresponding correct output or label. By learning from this dataset, the model can later apply its knowledge to predict outcomes for new, unseen data.

In supervised learning, the presence of labels is crucial, as they guide the training process by indicating the desired output for each input example. This allows the model to adjust its parameters based on the errors it makes during training, ultimately improving its accuracy in making predictions.

The other choices highlight different learning paradigms. For instance, one describes a scenario where machines learn independently, which aligns more closely with unsupervised learning. Another option refers to a learning method based on human behavior observation, also suggesting a more observational approach that characterizes semi-supervised or reinforcement learning—not supervised learning. The last option talks about the use of pre-existing algorithms without training data, which does not involve any learning process and contradicts the principles of supervised learning altogether.