Database Reference
In-Depth Information
CHAPTER NINE:
LOGISTIC REGRESSION
CONTEXT AND PERSPECTIVE
Remember Sonia, the health insurance program director from Chapter 6? Well, she's back for
more help too! Her k-means clustering project was so helpful in finding groups of folks who could
benefit from her programs, that she wants to do more. This time around, she is concerned with
helping those who have suffered heart attacks. She wants to help them improve lifestyle choices,
including management of weight and stress, in order to improve their chances of
not
suffering a
second heart attack. Sonia is wondering if, with the right training data, we can predict the chances
of her company's policy holders suffering second heart attacks. She feels like she could really help
some of her policy holders who have suffered heart attacks by offering weight, cholesterol and
stress management classes or support groups. By lowering these key heart attack risk factors, her
employer's clients will live healthier lives, and her employer's risk at having to pay costs associated
with treatment of second heart attacks will also go down. Sonia thinks she might even be able to
educate the insured individuals about ways to save money in other aspects of their lives, such as
their life insurance premiums, by being able to demonstrate that they are now a lower risk policy
holder.
LEARNING OBJECTIVES
After completing the reading and exercises in this chapter, you should be able to:
Explain what logistic regression is, how it is used and the benefits of using it.
Recognize the necessary format for data in order to perform predictive logistic regression.
Develop a logistic regression data mining model in RapidMiner using a training data set.
Interpret the model's outputs and apply them to a scoring data set in order to deploy the
model.
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