supervised learning

Supervised learning is a machine learning approach where a model is trained using a dataset that contains input-output pairs, with the outputs being the correct answers or labels for the inputs. The goal is for the model to learn to predict the correct output for new, unseen inputs based on the patterns it recognized during training. This method is commonly used for tasks like predicting numerical values (regression) or categorizing data into different groups (classification). For example, in a supervised learning task, a model could be trained to recognize whether an email is spam or not by learning from a set of emails that have already been labeled as ‘spam’ or ‘not spam’[2][3][4][5].

Citations:

[1] https://www.ibm.com/blog/supervised-vs-unsupervised-learning/

[2] https://www.explorium.ai/blog/machine-learning/supervised-learning/

[3] https://www.wevolver.com/article/unsupervised-vs-supervised-learning-a-comprehensive-comparison

[4] https://en.wikipedia.org/wiki/Supervised_learning

[5] https://www.geeksforgeeks.org/supervised-unsupervised-learning/

[6] https://domino.ai/blog/supervised-vs-unsupervised-learning

[7] https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/

[8] https://www.alteryx.com/glossary/supervised-vs-unsupervised-learning