Logistic regression is a classification algorithm used to assign observations to a discrete set of

classes. 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.
Types of Logistic Regression
1. Binary Logistic Regression

The categorical response has only two 2 possible outcomes. Example: Spam or Not

2. Multinomial Logistic Regression

Three or more categories without ordering. Example: Predicting which food is preferred
more (Veg, Non-Veg, Vegan)

3. Ordinal Logistic Regression

Three or more categories with ordering. Example: Movie rating from 1 to 5
Decision Boundary
To predict which class a data belongs, a threshold can be set. Based upon this threshold, the obtained estimated probability is classified into classes.

Visualization for decision boundary of 2 Classes