Supervised learning algorithms are a type of machine learning algorithm that are trained on labeled data to learn a mapping function from input variables (features) to output variables (labels). In other words, supervised learning algorithms are given a set of input/output pairs, and they learn to predict the output for new, unseen inputs.
The labeled data used for training a supervised learning algorithm typically consists of input data (features) and corresponding output data (labels), which are known as the training set. During training, the algorithm adjusts its internal parameters to minimize the difference between its predictions and the actual labels in the training set.
Once the algorithm is trained, it can be used to predict the output for new, unseen inputs. This process is known as inference or prediction.
Some examples of popular supervised learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
Supervised learning algorithms are commonly used in a wide range of applications, including image recognition, speech recognition, natural language processing, and recommendation systems, among others.