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Supervised learning involves learning a mapping from inputs to outputs from "summary" of Machine Learning by Stephen Marsland
Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset. This means that each input in the dataset is paired with the correct output, allowing the algorithm to learn the relationship between inputs and outputs. The goal of supervised learning is to learn a mapping from inputs to outputs, which can then be used to predict the output for new, unseen inputs. In supervised learning, the algorithm learns from the labeled dataset by adjusting its internal parameters to minimize the difference between the predicted output and the true output. This is done through an iterative process where the algorithm makes predictions on the training data, compares them to the true outputs, and updates its parameters accordingly. The algorithm continues this process until it has learned a mapping that accurately predicts the outputs for the inputs in the training data. The key idea in supervised learning is that the algorithm learns a mapping from inputs to outputs based on the information provided in the labeled dataset. This mapping allows the algorithm to generalize to new, unseen inputs and make predictions on them. By learning this mapping, the algorithm can perform tasks such as classification, regression, and prediction.- Supervised learning involves learning a mapping from inputs to outputs using a labeled dataset. The algorithm learns this mapping by adjusting its internal parameters to minimize the difference between predicted outputs and true outputs. This allows the algorithm to generalize to new inputs and make predictions on them based on the learned mapping.
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