What type of learning involves labeled data to train models?

Supervised learning is a type of machine learning that specifically uses labeled data to train models. In this approach, each training example is paired with an output label, allowing the algorithm to learn the relationship between the input data and the output labels. This enables the model to make predictions on new, unseen data by applying the learned mapping from inputs to outputs.

The process typically involves splitting a dataset into two parts: a training set, used to train the model, and a test set, used to evaluate its performance. The goal of supervised learning is to minimize the error of predictions during the training phase and generalize well on the test data, enabling accurate predictions when applied to new data.

In contrast, unsupervised learning deals with unlabeled data, where the goal is to find hidden patterns or intrinsic structures in the data without any prior knowledge of outcomes. Reinforcement learning is centered around training models through feedback mechanisms in an environment, optimizing actions based on rewards. Deep learning, while it can involve supervised learning techniques, is more of a subset of machine learning techniques that utilize neural networks with many layers to improve task performance.