How do classification and regression differ?

The distinction between classification and regression fundamentally lies in the nature of the predictions they produce. Classification is a type of problem where the outcome variable is categorical—essentially, it deals with predicting discrete labels or classes. For instance, in a scenario where you are tasked with classifying emails as "spam" or "not spam," the model will output one of these two discrete labels based on the features learned from the training data.

On the other hand, regression is used for predicting continuous values. For example, predicting house prices based on various features (like size, location, and amenities) would require a regression approach, as the output is a continuous numerical value that can vary over a range. The differences in output types—discrete versus continuous—are central to understanding when to apply classification versus regression in machine learning.

The other options present misunderstood concepts related to the definitions of classification and regression. For example, suggesting that classification predicts images while regression predicts videos inaccurately interprets the scope of these tasks, as both can deal with various data types. Similarly, classifying the relationship as supervised versus unsupervised is misleading; both are typically supervised learning techniques. Lastly, the classification of data types like time-series versus tabular data does not inherently determine whether