Results explanation
As before, once we pass the model evaluation stage and select the estimated model as our final model, the next task is to interpret the results for the company executives and technicians.
In the next section, we will work on results explanation focusing on some big influencing variables. With the big influencing variables identified, the company could use them to improve their marketing effort to recruit the right customers.
Big influencers and their impacts
With logistic regression results, we can explain the impact of each feature by using regression coefficients, and identify big influencers by comparing those coefficients.
With the same logic, we can also rank each feature by its effects as calculated by the logistic regression coefficients.
Another way is to use the R package of effect, which was created by John Fox and others especially for the display of the effects of linear and generalized linear models. By using this package, we can obtain a list and some graphical...
