What type of learning involves learning from labeled data to make predictions?

Supervised learning is the type of learning that involves training a model on a labeled dataset, where the input data is paired with the corresponding correct output or label. The primary goal of supervised learning is to learn a mapping from inputs to outputs so that the model can make accurate predictions on new, unseen data.

In this approach, the algorithm is provided with numerous examples of input-output pairs, allowing it to understand the relationship between the features of the data and the outcomes. Once trained, the model can then generalize from the training data to make predictions or classifications for new instances based on the patterns it learned from the labeled data.

This method is fundamental in machine learning applications such as classification and regression tasks, where the accuracy of predictions can be quantified using various metrics by comparing the model's outputs to the actual labels in the test data. The success of many practical AI applications relies on leveraging labeled datasets to improve the model's performance and fidelity in real-world scenarios.

The other options represent different paradigms of learning. Unsupervised learning deals with finding patterns in data without labeled outcomes, reinforcement learning involves learning through trial and error in dynamic environments to maximize cumulative rewards, and deep learning is a subset of machine learning that uses neural networks but can also operate