Logistic Regression
Logistic regression uses categorical and continuous variables to predict a categorical outcome. When the dependent variable of choice has two categorical outcomes, the analysis is termed binary logistic regression. However, if the outcome variable consists of more than two levels, the analysis is referred to as multinomial logistic regression. For the purposes of this chapter, we will focus our learning on the former.
When predicting a binary outcome, we do not have a linear relationship between the features and the outcome variable; an assumption of linear regression. Thus, to express a nonlinear relationship in a linear way, we must transform the data using logarithmic transformation. As a result, logistic regression allows us to predict the probability of the binary outcome occurring given the feature(s) in the model.
For logistic regression with 1 predictor, the logistic regression equation is shown here:
