Supervised learning involves training algorithms on labeled data from "summary" of Machine Learning by Ethem Alpaydin
In supervised learning, algorithms are trained using labeled data. This means that the input data given to the algorithm is accompanied by the correct output for that data. The algorithm learns to map the input to the output by generalizing from the labeled data. The goal is to make accurate predictions on new, unseen data based on the patterns learned from the labeled data during training. The process of supervised learning involves feeding the algorithm a training set of labeled examples. The algorithm then learns from these examples to make predictions on new, unseen data. The labeled data serves as a guide for the algorithm to learn the underlying patterns and relationships in the data. By training on labeled data, the algorithm can adjust its internal parameters to minimize the error between its predictions and the true labels. The key distinction ...
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