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 in supervised learning is the presence of labeled data. This supervision is crucial for the algorithm to learn how to generalize from the training data to make predictions on unseen data. Without labeled data, the algorithm would have no guidance on how to make accurate predictions. Labeled data provides the necessary feedback for the algorithm to adjust its model and improve its predictions over time. By training on labeled data, supervised learning algorithms can learn to recognize patterns, classify data into different categories, and make predictions on new instances. The labeled data acts as a teacher for the algorithm, guiding it towards better performance on unseen data. Supervised learning is a powerful technique in machine learning that has been successfully applied to a wide range of tasks, from image recognition to natural language processing.
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