regression model

Informally, a model that generates a numerical prediction. (In contrast, a classification model generates a class prediction.) For example, the following are all regression models: - A model that predicts a certain house's value in Euros, such as 423,000. - A model that predicts a certain tree's life expectancy in years, such as 23.2. - A model that predicts the amount of rain in inches that will fall in a certain city over the next six hours, such as 0.18. Two common types of regression models are: - Linear regression, which finds the line that best fits label values to features. - Logistic regression, which generates a probability between 0.0 and 1.0 that a system typically then maps to a class prediction. Not every model that outputs numerical predictions is a regression model. In some cases, a numeric prediction is really just a classification model that happens to have numeric class names. For example, a model that predicts a numeric postal code is a classification model, not a regression model.

Practitioners refer to regression model when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.