Supervised learning is a machine learning technique in which a model is trained to learn to map inputs to corresponding outputs, using a set of labelled data. The labelled data consists of examples of inputs, also called features or independent variables, along with their corresponding outputs, also called labels or dependent variables.
In supervised learning, the machine learning model learns to generalise from the labelled examples so that it can predict outputs corresponding to new inputs never seen before. The goal is that the model can learn a function that effectively maps inputs to corresponding outputs.
Supervised learning is used in applications such as image classification, speech recognition, machine translation and stock price prediction, among others. Many supervised learning algorithms have been developed, including decision trees, linear regression, neural networks and support vector machines, among others.
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