Do you want to know What is SVM? and What makes SVM very powerful and Special in Machine Learning? If yes, then give your few minutes to this blog to know What makes SVM very Powerful and Special in Machine Learning?

Hello, & Welcome!

In this blog, I am gonna tell you-

- What is SVM in Machine Learning?
- How SVM Works?
- What makes SVM very Powerful and Special?

Firstly, I would like to start with-

**What is SVM in Machine Learning?**

SVM is developed in the 1960s and refined in the 1990s. It become very popular in the machine learning field because SVM is very powerful as compare to other algorithms.

SVM ( Support Vector Machine) is a supervised machine learning algorithm. As SVM is Supervised, that’s why training data is available to train the model. SVM uses a classification algorithm to classify a two group problem. SVM focus on decision boundary and support vectors, which we will discuss in the next section.

**How SVM Works?**

Here, we have two points in two-dimensional space, we have two columns x1 and x2. And we have some observation as red and green, which are already classified. This is linearly separable data.

But, now how do we derive a line that separates these points. This means a separation or decision boundary is very important for us when we add new points. SVM has to classify these new points as on which class it belongs to. This is the purpose of our classification.

In order to classify, create a boundary between two categories, so when in future we add new points and we want to classify them, then we know where they belong. Either in a Green Area or in Red Area.

### So how we can separate these points?

One way is to draw a vertical line between two areas, so anything in the right is Red and anything in the left is Green. Something like that-

However, there is one more way, draw a horizontal line or diagonal line. You can create multiple diagonal lines, which achieve the similar results to separate our points into two classes. But they all have different consequences in the future, when we add new points. And depending upon whether the point falls, it will add to the class, whether its green or red. But the main task is to find the optimal line or best decision boundary. And SVM is all about finding the best decision boundary, which helps us to separate points into different spaces.

Let’s see,

**How SVM Search for Best Line**?

You can search the best line through the maximum margin, which means it has max distance and equidistance from both classes or spaces. The sum of these two classes has to be maximized in order to make this line as maximum margin.

These, two vectors are **support vectors. **If you remove the rest of the points, still SVM works the same, nothing will change. Other points don’t contribute. Only support vectors are contributing to the SVM algorithm. That’s why these points or vectors are known as **support vectors**. Due to support vectors, this algorithm is called A Support Vector Algorithm(SVM).

In the picture, the line in the middle is a **maximum margin hyperplane** or classifier. In a two-dimensional plane, it looks like a line, but in multi-dimensional it is a hyperplane. That’s how SVM works.

**What makes SVM very Powerful and Special?**

After learning about SVM and how it works, its time to know why SVM is very powerful and different as compared to other machine learning algorithms.

Are you excited to know?

Let’s get started.

Imagine you are trying to teach a machine how to distinguish between an apple and orange. How to classify fruit into apple and orange?

So you tell the machine that I am giving you data and have a look at this data, look on apple data and orange data, then analyze these data. After that, I will give you an image and you have to predict whether it is an apple or orange. That is a standard machine learning problem.

What other machine learning algorithms do- it looks on most stock standard apples or most common types of apples and most common types of oranges. In short, they look at the most common types of apples and oranges. And machines try to learn from most common apples and from oranges. Which are very far from each other. Most machine learning algorithm does in the same way and predicts a result.

### But,

In SVM, it is a bit different. Instead of looking at the most stock standard apples and oranges, SVM look at the apples which is much like an orange, Which means an apple who doesn’t have common features, it looks something different and similar to orange. Similarly, SVM look on oranges which look like an apple, or different in color and shape. Therefore, it is very easy to confuse an apple as an orange or vise versa.

SVM make these type of apples and oranges as a support vector, they are very close to the boundary. That’s why SVM is a very extreme type of algorithm because SVM focuses on an extreme case. SVM uses these vectors to construct an analysis.

This feature makes SVM very special and very different from other algorithms, and that’s why SVM performs much better than Non-SVM.

I hope, now you have a clear idea about What is SVM algorithms? and What makes SVM very Powerful and Special in machine learning?.

Are you ML Beginner and confused, from where to start ML, then read my BLOG – How do I learn Machine Learning?

If you are looking for Machine Learning Algorithms, then read my Blog – Top 5 Machine Learning Algorithm.

If you are wondering about Machine Learning, read this Blog- What is Machine Learning?

Enjoy Machine Learning

All the Best!

**Wanna Learn Basics of ML?. Learn Here.**

Thank YOU!

## Though of the Day…

–

‘It’s what you learn after you know it all that counts.’John Wooden

### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.