Definition:
Logistic Regression is a Supervised learning algorithm that makes use of logistic functions to predict the probability of a Binary outcome.
- outcome limited to two possible outcomes: yes/no, 0/1, or true/false.
Logistic regression analyzes the relationship between one or more independent variables and classifies data into discrete classes. It is extensively used in predictive modeling, where the model estimates the mathematical probability of whether an instance belongs to a specific category or not.
Logistic regression uses a logistic function called a sigmoid function to map predictions and their probabilities. The sigmoid function refers to an S-shaped curve that converts any real value to a range between 0 and 1.
Types of Logistic Regression:
- Binary Logistic Regression:
The dependent variable has only two 2 possible outcomes/classes.
Example-Male or Female. - Multinomial Logistic Regression:
The dependent variable has only two 3 or more possible outcomes/classes without ordering.
Example: Predicting food quality.(Good,Great and Bad). - Ordinal Logistic Regression:
The dependent variable has only two 3 or more possible outcomes/classes with ordering. Example: Star rating from 1 to 5
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