What type of learning involves using labeled data to train a model?

Supervised learning involves using labeled data to train a model. In this learning approach, each training example is accompanied by an output label, allowing the algorithm to learn the relationship between the input data and the corresponding output values. This process typically includes providing the model with a dataset that contains input-output pairs, which helps it understand how to make predictions or classifications based on new, unseen data.

Supervised learning is commonly used in scenarios such as classification tasks (like identifying whether an email is spam or not) and regression tasks (like predicting house prices based on various features). The ability to continuously check the model’s predictions against the known labels enables it to improve its performance over time.

In contrast, the other types of learning do not rely on labeled data. Unsupervised learning works with datasets where the output labels are unknown, focusing instead on identifying patterns or groupings within the data. Reinforcement learning emphasizes learning through trial and error to achieve specific goals in an environment, rather than using labeled examples. Active learning allows the model to query for labels on uncertain data points but still relies on labeled information for training.