How do classification problems differ from regression problems?

Classification problems and regression problems are distinct in terms of the type of outcomes they predict. When working with classification, the goal is to assign data into predefined categories or classes. For example, when you classify emails as "spam" or "not spam," you are dealing with categorical outcomes, which may consist of multiple distinct classes.

In contrast, regression problems focus on predicting a continuous numerical outcome. This might include predicting prices, temperatures, or any other quantity that can take on an infinite range of values, not confined to categories.

The correct understanding of classification as predicting categorical outcomes is key in differentiating it from regression, which is essential for selecting the appropriate analytical methods and algorithms in predictive modeling tasks. By knowing that classification is about categories, you can apply techniques that are tailored to handle such types of data effectively.