What type of machine learning involves predicting a continuous output?

Regression is the type of machine learning that focuses on predicting a continuous output. In regression tasks, the goal is to model the relationship between one or more independent variables (also known as features or predictors) and a dependent variable (the output). For instance, predicting the price of a house based on its characteristics such as size, location, and age falls under regression because the price is a continuous numerical value.

This method utilizes various algorithms to understand how the dependent variable changes with changes in the independent variables, enabling predictions for new, unseen data points. Common regression techniques include linear regression, polynomial regression, and more advanced models like regression trees and neural networks designed for regression tasks.

In contrast, classification involves predicting discrete labels or categories, clustering focuses on grouping similar data points without predefined labels, and dimensionality reduction aims to simplify datasets by reducing the number of features while preserving essential information. These concepts differentiate them from regression, which is specifically about forecasting continuous outputs.