While classification is the problem of predicting a discrete class label for an example, regression predictive modeling (or just regression) is the problem of learning the strength of association between independent variables (or features) and continuous dependent variables (or outcomes). A continuous output variable is a real value such as an integer or floating point value often quantified as amounts and sizes.
Simply, regression attempts to learn how strong the relationship is between features and outcomes. Formally, regression approximates a mapping ...
