Logistic regression is a type of regression model where the dependent or class variable is not continuous but categorical, just as in our case, credit rating is the dependent variable with two classes. In principle, logistic regression is usually perceived as a special case of the family of generalized linear models. This model functions by trying to find out the relationship between the class variable and the other independent feature variables by estimating probabilities. It uses the logistic or sigmoid function for estimating these probabilities. Logistic regression does not predict classes directly but the probability of the outcome. For our model, since we are dealing with a binary classification problem, we will be dealing with binomial logistic regression.
First we will load the library dependencies as follows and separate the testing feature and class variables:
library(caret) # model training and evaluation library(ROCR) # model evaluation source...
