supervised learning

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supervised learning

Supervised learning is a type of machine learning where algorithms are trained using labeled data. This means that the training dataset includes both the input features and the corresponding correct output labels. The goal is for the model to learn the relationship between the inputs and outputs so that it can make accurate predictions on new, unseen data.

In supervised learning, there are two main types of problems: classification and regression. Classification involves predicting discrete labels, such as determining whether an email is “Spam” or “Not Spam.” Regression, on the other hand, deals with predicting continuous values, such as estimating the price of a car based on its features ²³.

The process typically involves feeding the model a large amount of historical data, allowing it to learn patterns and make predictions based on that training. For example, a supervised learning model could be trained with images of cats and dogs, enabling it to distinguish between the two in future images ²³.

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