![]() Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
May 2023
Categories |