Predictive variables, also known as independent variables, are variables whose values are used in an equation to calculate a value for the target. For example, suppose we wanted to predict a person’s weight in one week based on their current age, gender, height, recent weight change, and waist measurements. In this example, the target variable is weight upon rising before breakfast and the predictive variables are:
- Current age,
- Current Height (in inches),
- Weight change over the past week in pounds upon rising before breakfast,
- Current Waist measurement (maximum in inches).
In a modeling project, there are usually a large number of potential predictive variables and only few of them will end up being implemented in the predictive equation. In most cases, predictive equations are predicting some future value or event, so that the predictive variables are either current or historical values. In the above example, the time frame is relatively short so the difference in the predictive values over that time may not be important unless the model is based on a sample of individuals that are on a diet.
The exact definition of the predictive variables is also important. In this example, the time of day of both the weight change and the target values are defined at a given time of day under specific conditions. This is because daily fluctuations in weight can influence the results.