# Types of Regression Models

## Types of Regression Models in Machine Learning

Regression modeling is a process of determining a relationship between one or more independent variables and one dependent or output variable.

Examples:

1. Predicting the price of the car given the car model, year of manufacturing, mileage, engine capacity, etc.

2. Predicting the height of a person given the age of the person.

### Types of Regression Models

Based on the type of functions used to represent the relationship between the dependent or output variable and independent variables, the regression models are categorized into four types. The regression models are,

1. Simple linear regression

2. Multiple regression

3. Polynomial regression

4. Logistic regression

### 1. Simple Linear Regression

Assume that there is only one independent variable x. If the relationship between x (independent variable) and y (dependent or output variable) is modeled by the relation,

y = a + bx

then the regression model is called a linear regression model.

### 2. Multiple Regression

Assume that there are multiple independent variables say x1, x2, ….xn. If the relationship between independent variables x and dependent or output variable y is modeled by the relation,

y=a0+a1*x1+a2*x2+………+an*xn

then the regression model is called a multiple regression model.

### 3. Polynomial Regression

Assume that there is only one independent variable x. If the relationship between independent variables x and dependent or output variable y is modeled by the relation,

y=a0+a1*x+a2*x2 +………+an*xn

for some positive integer n >1, then we have a polynomial regression.

Solved numerical example on Polynomial Regression.

### 4. Logistic Regression

Logistic regression is used when the dependent variable is binary (0/1, True/False, Yes/No) in nature. Even though the output is a binary variable, what is being sought is a probability function that may take any value from 0 to 1.

### Summary

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