A team has 100,000 historical customer support emails that are already labeled with the sentiment positive or negative. They want a model that predicts sentiment for new, unseen emails. Which learning method should they choose?
Self-supervised learning
Unsupervised learning
Reinforcement learning
Supervised learning
Because each training example already carries a known sentiment label, the data scientist can map inputs (emails) to outputs (sentiment classes). This mapping of labeled examples is exactly the definition of supervised learning. Unsupervised learning does not require labels and would instead look for inherent structure, reinforcement learning optimizes behavior through trial-and-error feedback, and self-supervised learning infers labels from the data itself rather than using human-supplied annotations.
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