In supervised learning, the main difference between regression and classification lies in the type of output they predict:
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Regression: The goal is to predict a continuous target variable. For example, predicting house prices based on features like size, location, and number of bedrooms.
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Classification: The goal is to predict the class or category of an observation. For example, determining whether an email is spam or not based on its content.
In summary, regression deals with continuous outputs, while classification deals with discrete categories.
