# What is support vector regression (SVR) ?

## An introduction to machine learning algorithms

A **support vector regression **is a popular **machine** **learning** **model** today in this article, I would be giving you a **detailed explanation** and how this model works.

**support vector regression** comes in the field of **supervised learning.**

# Support vector machine and regression

**Support** **vector** **model** can be used for both problems **regression** as well as **classification** and it’s divided into **2 parts support vector machine (SVM)**is used for **classification** problems and **support vector regression (SVR) **is mostly used for **regression problems** but in this article, I would be telling you about **support vector regression (SVR)** to know more about **support vector machine (SVM)** go to this link

so first of all

## What is a hyperplane?

the hyperplane is the boundary between two classes that separates them from each other and the hyperplane can be dependent on the dataset if our data is 2 dimensional the hyperplane will be a line and if our data is 3 dimensional then the hyperplane will be a 2d plane which separates the data points

## How does it work

**SVR** finds a **hyperplane** in an **n-dimension **or a **line** to

**classify** between the **dataset** so when we add a new point in the dataset the SVM can tell what class the point belongs to

# Types of SVR

# linear SVR

**linear SVR** when our data is **linear** then linear SVR and we can separate them from a **straight** **line**

# non-linear SVR

**non-linear SVR **when our data is **non-linear** then we use non-linear **SVR **and it is not separable by a straight line but instead can be separated by a **curve**

# Example

## Linear SVR

in **linear** **SVR** when our data looks like this we can plot a line to make a relationship so we will plot a line between our dataset so we can get a relation between the dataset then there are many **lines** we can draw that can pass between all of the points in our data but to find the best line

we need to introduce a **hyperplane** which consists of mostly all the **lines which** separates the data and **support vectors **helps them to build the **hyperplane**

## Non-linear SVR

**non-linear SVR** in the linear SVR we used a line to find the relationship between our data but in **non-linear SVR **when our data looks something like this and cannot be separated by a line we need to introduce a **curve** to find the relationship between the data

# conclusion

so I hope today you guys have a good understanding of** support vector regression** in the near future I would be making more articles in which I will be **explaining** **more** **models**.