A regression problem predicts continuous numerical values, such as sales figures or temperatures, while a classification problem categorizes data into discrete labels, like spam or non-spam emails. The key to distinguishing between them lies in the output: if it's a numerical prediction (e.g., house prices), it's a regression problem; if it's a category (e.g., yes/no, type of product), it's a classification problem. Understanding this distinction helps in applying the correct algorithms and models. For example, predicting future stock prices is a regression task, while sorting emails into spam or not is a classification task.

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