is a discriminative-typed supervised classification algorithm that models the relationship between:
- a single discrete/categorical response/dependent variable 𝑌 (for continuous/scalar use linear regression)
- one or more explanatory/predictor/covariate/independent variables {𝑋₁, ..., 𝑋_𝑘}. predictor variable types:
  - continuous/scalar/numerical predictor
  - discrete/categorical predictor - itself can be either bi/multi-nominal or ordinal
unlike linear regression which outputs continuous number values, logistic regression transforms its output using the logistic sigmoid function to return a probability value which can then be mapped to two or more discrete classes

Logistic Regression - Types

Type	Description
Binomial/Binary Logistic Regression (BLR)	nominal target variable can have only 2 possible types: “0” or “1” e.g. “win” vs “loss”, “pass” vs “fail”, “dead” vs “alive”, etc
Multinomial/Nominal Logistic Regression (MLR)	nominal target variable can have 3 or more possible types which are not ordered (i.e. types have no quantitative significance) e.g. “disease A” vs “disease B” vs “disease C”
Ordinal Logistic Regression	ordinal/ordered target variable e.g. a test score can be categorized as: “very poor”, “poor”, “good”, “very good”. Here, each category can be given a score like 0, 1, 2, 3

Logistic Regression - Other