What defines regression models in predictive analytics?

Regression models in predictive analytics are defined by their ability to predict continuous outcomes from predictor variables. This involves establishing a relationship between independent (predictor) variables and a dependent (outcome) variable, which is continuous in nature. For instance, regression models can be used to forecast sales figures, temperatures, or any measurable quantity that can take on a range of values, making them integral to various predictive analytics tasks.

The focus on continuous outcomes is what sets regression apart from other modeling techniques, such as classification models, which are designed to handle categorical output. Regression also does not specifically pertain to time-to-event data, which is more relevant for survival analysis or time series forecasting. Similarly, similarity grouping pertains to clustering techniques, which categorize elements based on similarity rather than predicting numeric outcomes. This distinction highlights the unique role that regression models play within the broader landscape of predictive analytics.