The examples so far have been of continuous variables. However, other variables are discrete and can be of a binary type. Some common examples of discrete binary variables are if it is snowing in a city on a given day or not, if a patient is carrying a virus or not, and so on. One of the main differences between binary logistic and linear regression is that in binary logistic regression, we are fitting the probability of an outcome given a measured (discrete or continuous) variable, while linear regression models deal with characterizing the dependency of two or more continuous variables on each other. Logistic regression gives the probability of an occurrence given some observed variable(s). Probability is sometimes expressed as P(Y|X) and read as Probability that the value is Y given the variable X.
Algorithms that guess the discrete outcome are called classification algorithms and are a part of machine learning techniques, which will be covered later in the book.
The...