Supervised learning is a type of machine learning in which an algorithm is trained on a labeled dataset to make predictions or decisions. The labeled dataset includes both input features (also known as independent variables) and output labels (also known as dependent variables). The algorithm learns to map the input features to the output labels through a process of optimization. Once the algorithm is trained, it can be used to predict the output label for new input observations that it has not seen before. Supervised learning algorithms can be used for tasks such as classification (predicting a categorical label) and regression (predicting a continuous numerical value).
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