Supervised learning is a type of machine learning in which an algorithm is trained on a labeled dataset consisting of input/output pairs. The goal of supervised learning is to learn a mapping from input to output based on the labeled examples in the dataset, so that the algorithm can make predictions on new, unseen data.
Supervised learning can be further divided into two categories: classification and regression. In classification, the output variable is a categorical variable, and the goal is to learn a function that maps inputs to their corresponding categories. In regression, the output variable is a continuous variable, and the goal is to learn a function that predicts the output value given the input.
Some common algorithms used in supervised learning include decision trees, random forests, logistic regression, support vector machines (SVMs), and neural networks. These algorithms use different techniques to learn the mapping from input to output, and they can be tuned using various hyperparameters to optimize their performance on the training data.
Supervised learning has many applications in various fields, such as image and speech recognition, natural language processing, recommendation systems, and predictive maintenance
