Database Reference
In-Depth Information
contacts. With such insight, target the high-probability customers with
appropriate offers to prevent churn.
•
Engineering:
Based on operating conditions and various diagnostic
measurements, determine the probability of a mechanical part
experiencing a malfunction or failure. With this probability estimate,
schedule the appropriate preventive maintenance activity.
6.2.2 Model Description
Logistic regression is based on the logistic function
, as given in
Equation 6.7
.
Note that as
. So, as
Figure 6.14
illustrates, the value of the logistic function
varies from 0 to 1 as y increases.
Figure 6.14
The logistic function
Because the range of is (0, 1), the logistic function appears to be an appropriate
function to model the probability of a particular outcome occurring. As the value
of y increases, the probability of the outcome occurring increases. In any proposed
model, to predict the likelihood of an outcome, y needs to be a function of the input
variables. In logistic regression, y is expressed as a linear function of the input
variables. In other words, the formula shown in
Equation 6.8
applies.
