Supervised learning is a type of machine learning where the model is trained on a labeled dataset, meaning the input data is paired with the correct output. The goal of supervised learning is to learn a mapping from inputs to outputs, so that the model can make predictions on new, unseen data.
In supervised learning, the training dataset consists of input-output pairs, where the inputs are the features or attributes of the data, and the outputs are the labels or target values. The model learns to associate the inputs with the correct outputs by finding patterns and relationships in the data.
Supervised learning is used in a wide range of applications, such as image recognition, speech recognition, and natural language processing. It is one of the most common types of machine learning and forms the basis for many other machine learning techniques.
