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,
- Gender,
- 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.

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