Database Reference
In-Depth Information
6.2 Logistic Regression
In linear regression modeling, the outcome variable is a continuous variable. As
seen in the earlier
Income
example, linear regression can be used to model the
relationship between age and education to income. Suppose a person's actual
income was not of interest, but rather whether someone was wealthy or poor. In
such a case, when the outcome variable is categorical in nature, logistic regression
can be used to predict the likelihood of an outcome based on the input variables.
Although logistic regression can be applied to an outcome variable that represents
multiple values, the following discussion examines the case in which the outcome
variable represents two values such as true/false, pass/fail, or yes/no.
For example, a logistic regression model can be built to determine if a person will
or will not purchase a new automobile in the next 12 months. The training set could
include input variables for a person's age, income, and gender as well as the age of
an existing automobile. The training set would also include the outcome variable
on whether the person purchased a new automobile over a 12-month period. The
logistic regression model provides the likelihood or probability of a person making
a purchase in the next 12 months. After examining a few more use cases for logistic
regression, the remaining portion of this chapter examines how to build and
evaluate a logistic regression model.
6.2.1 Use Cases
The logistic regression model is applied to a variety of situations in both the public
and the private sector. Some common ways that the logistic regression model is used
include the following:
•
Medical:
Develop a model to determine the likelihood of a patient's
successful response to a specific medical treatment or procedure. Input
variables could include age, weight, blood pressure, and cholesterol levels.
•
Finance:
Using a loan applicant's credit history and the details on the
loan, determine the probability that an applicant will default on the loan.
Based on the prediction, the loan can be approved or denied, or the terms
can be modified.
•
Marketing:
Determine a wireless customer's probability of switching
carriers (known as churning) based on age, number of family members on
the plan, months remaining on the existing contract, and social network
