Mathematically, a binary logistic model has a dependent variable with two categorical values. In our example, these values relate to whether or not a bank is solvent.
In a logistic model, log odds refers to the logarithm of the odds for a class, which is a linear combination of one or more independent variables, as follows:
The coefficients (beta values, β) of the logistic regression algorithm must be estimated using maximum likelihood estimation. Maximum likelihood estimation involves getting values for the regression coefficients that minimize the error in the probabilities that are predicted by the model and the real observed case.
Logistic regression is very sensitive to the presence of outlier values, so high correlations in variables should be avoided. Logistic regression in R can be applied as follows:
set.seed(1234)
LogisticRegression=glm(train...
