What is the primary difference between classification and regression tasks in machine learning?

The primary difference between classification and regression tasks in machine learning lies in the type of output they predict. Classification focuses on predicting categorical outcomes, which are discrete labels or categories. For example, in a classification task, the goal might be to determine whether an email is 'spam' or 'not spam'. This involves assigning the input data to one of a finite set of categories based on its features.

On the other hand, regression tasks are concerned with predicting continuous outcomes. This means that the model outputs a numerical value, which can take any value within a range. An example of a regression task could be predicting the price of a house based on its features, such as size, location, and number of bedrooms.

This distinction is fundamental in machine learning, as it influences the choice of algorithms and evaluation metrics used for each type of task. Classification problems often utilize metrics such as accuracy, precision, and recall, whereas regression problems use metrics like mean squared error or R-squared to assess model performance. Understanding this difference is crucial for correctly framing a problem in the context of machine learning applications.