Support Vector Machine Algorithm in Machine Learning

Everything You Need to Know about Support Vector Machine Algorithms

Most beginners, when it comes to machine learning, start with regression and classification algorithms naturally. These algorithms are simple and easy to follow. However, it is essential to go beyond these two machine learning algorithms to grasp the concepts of machine learning better.

There is much more to learn in machine learning, which might not be as simple as regression and classification, but can help us solve various complex problems. Let us introduce you to one such algorithm, the Support Vector Machine AlgorithmSupport Vector Machine algorithm, or SVM algorithm, is usually referred to as one such machine learning algorithm that can deliver efficiency and accuracy for both regression and classification problems.

If you dream of pursuing a career in the machine learning field, then the Support Vector Machine should be a part of your learning arsenal. At upGrad, we believe in equipping our students with the best machine learning algorithms to get started with their careers. Here’s what we think can help you begin with the SVM algorithm in machine learning.

What is a Support Vector Machine Algorithm?

SVM is a type of supervised learning algorithm that has become very popular in 2020 and will continue to be so in the future. The history of SVM dates back to 1990; it is drawn from Vapnik’s statistical learning theory. SVM can be used for both regression and classification challenges; however, it is mostly used for addressing classification challenges.

SVM is a discriminative classifier that creates hyperplanes in N-dimensional space, where n is the number of features in a dataset to help discriminate future data inputs. Sounds confusing right, don’t worry, we’ll understand it in simple layman terms.

How Does a Support Vector Machine Algorithm Work?

Before delving deep into the working of an SVM, let’s understand some of the key terminologies.


Hyperplanes, which are also sometimes referred to as decision boundaries or decision planes, are the boundaries that help classify data points. The hyperplane’s side, where a new data point falls, can be segregated or attributed to different classes. The dimension of the hyperplane depends on the number of features that are attributed to a dataset. If the dataset has 2 features, then the hyperplane can be a simple line. When a dataset has 3 features, then the hyperplane is a 2-dimensional plane.

Support Vectors

Support vectors are the data points that are closest to the hyperplane and affect its position. Since these vectors affect the hyperplane positioning, they are termed as support vectors and hence the name Support Vector Machine Algorithm.


Put simply, the margin is the gap between the hyperplane and the support vectors. SVM always chooses the hyperplane that maximizes the margin. The greater the margin, the higher is the accuracy of the outcomes. There are two types of margins that are used in SVM algorithms, hard and soft.

When the training dataset is linearly separable, SVM can simply select two parallel lines that maximize the marginal distance; this is called a hard margin. When the training dataset is not fully linearly separate, then the SVM allows some margin violation. It allows some data points to stay on the wrong side of the hyperplane or between the margin and hyperplane so that the accuracy is not compromised; this is called a soft margin.

There can be many possible hyperplanes for a given dataset. The goal of VSM is to select the most maximal margin to classify new data points into different classes. When a new data point is added, the SVM determines which side of the hyperplane the data point falls. Based on the side of the hyperplane where the new data point falls, SVM then classifies it into different classes.

Read: Linear Algebra for Machine Learning: Critical Concepts, Why Learn Before ML

What are the Types of Support Vector Machines?

Based on the training dataset, SVM algorithms can be of two types:

Linear SVM


Linear SVM is used for a linearly separable dataset. A simple real-world example can help us understand the working of a linear SVM. Consider a dataset that has a single feature, the weight of a person. The data points can are supposed to be classified into two classes, obese or not obese. To classify data points into these two classes, SVM can create a maximal-margin hyperplane with the help of the nearest support vectors. Now, whenever a new data point is added, the SVM will detect the hyperplane’s side, where it falls, and classify the person as obese or not.

Non-Linear SVM


As the number of features increases, separating the dataset linearly becomes challenging. That’s where a non-linear SVM is used. We cannot draw a straight line to separate data points when the dataset is not linearly separable. So to separate these data points, SVM adds another dimension. The new dimension z can be calculated as z = x2 + Y2. This calculation will help separate the features of a dataset in linear form, and then SVM can create the hyperplane to classify data points.

When a data point is transformed into a high dimension space by adding a new dimension, it becomes easily separable with a hyperplane. This is done with the help of what is called the kernel trick. With the kernel trick, SVM algorithms can transform non-separable data into separable data.

What is a Kernel?

A kernel is a function that takes low dimension inputs and transforms them into high dimension space. It is also referred to as a tuning parameter that helps to increase the accuracy of SVM outputs. They perform some complex data transforms to convert the non-separable dataset into a separable one.


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What are the Different Types of SVM Kernels?

Linear Kernel

As the name suggests, the linear kernel is used for linearly separable datasets. It is mostly used for datasets with a large number of features, text classification, for example, where all alphabets are a new feature. The syntax of the linear kernel is:

K(x, y) = sum(x*y)

x and y in the syntax are two vectors.

Training an SVM with a linear kernel is faster than training it with any other kernel as it requires optimization of only C regularization parameter and not the gamma parameter.

Polynomial Kernel

The polynomial kernel is a more generalized form of the linear kernel that is useful in transforming non-linear dataset. The formula of the polynomial kernel is as follow:

K(x, y) = (xT*y + c)d

Here x and y are two vectors, c is a constant that allows tradeoff for higher and lower dimension terms, and d is the order of the kernel. The developer is supposed to decide the order of the kernel manually in the algorithm.

Radial Basis Function Kernel

Radial basis function kernel, also referred to as Gaussian kernel, is a widely used kernel in SVM algorithms for solving classification problems. It has the potential to map input data into indefinite high dimensional spaces. The radial basis function kernel can be mathematically represented as:

K(x, y) = exp(-gamma*sum(x – y2))

Here, x and y are two vectors, and gamma is a tuning parameter ranging from 0 to 1. Gamma is pre-defined manually in the learning algorithm.

The linear, polynomial, and radial basis functions differ in their mathematical approach for making the hyperplane creation decisions and accuracy. Linear and polynomial kernels consume less time in training but provide less accuracy. On the other hand, the radial basis function kernel takes more time in training but provides higher accuracy in terms of results.

Now the question that arises is how to choose what kernel to use for your dataset. Your decision should solely depend on the complexity of the dataset and the accuracy of the results you want. Of course, everyone wants high accuracy results, but it also depends on the time you have to develop the solution and how much you can spend on it. Also, the radial basis function kernel generally provides higher accuracy, but in some circumstances, the linear and polynomial kernels can perform equally well.

For instance, for linearly separable data, a linear kernel will perform as well as a radial basis kernel and while consuming less training time. So if your dataset is linearly separable, you should choose a linear kernel. For non-linear data, you should choose a polynomial or radial basis function depending on the time and expense you have.

What are the Tuning Parameters Used with Kernels?

C regularization

The C regularization parameter accepts values from you to allow a certain level of misclassification in each training dataset. Higher C regularization values lead to small-margin hyperplane and do not allow much misclassification. Lower values, on the other hand, leads to high-margin and greater misclassification.


The gamma parameter defines the range of support vectors that will impact the positioning of the hyperplane. High gamma value considers only nearby data points, and low value considers far away points.

How to Implement the Support Vector Machine Algorithm in Python?


Since we have the basic idea of what the SVM algorithm is and how it works, let’s delve into something more complex. Now we will look at the general steps to implement and run the SVM algorithm in Python. We will be using the Scikit-Learn library of Python to learn how to implement the SVM algorithm.

First and foremost, we have to import all the necessary libraries such as Pandas and NumPy that are necessary to run the SVM algorithms. Once we have all the libraries in the place, we have to import the training dataset. Next, we have to analyze our dataset. There are multiple ways to analyze a dataset.

For instance, we can check the dimensions of data, divide it into response and explanatory variables, and set KPIs to analyze our dataset. After completing data analysis, we have to pre-process the dataset. We should check for irrelevant, incomplete, and incorrect data in our dataset.

Now comes the training part. We have to code and train our algorithm with the relevant kernel. The Scikit-Learn contains the SVM library, where you can find some built-in classes for training algorithms. The SVM library contains an SVC class that accepts the value for the type of kernel that you want to use to train your algorithms.

Then you call the fit method of the SVC class that trains your algorithm, inserted as the parameter to the fit method. You have then to use the predict method of the SVC class to make predictions for the algorithm. Once you have completed the prediction step, you have to call the classification_report and confusion_matrix of the metrics library to evaluate your algorithm and see the result.

What are the Applications of the Support Vector Machine algorithm?

SVM algorithms have applications across various regression and classification challenges. Some of the key applications of SVM algorithms are:

  • Text and hypertext classification
  • Image classification
  • Classification of satellite data such as Synthetic-Aperture Radar (SAR)
  • Classifying biological substances such as proteins
  • Character recognition in hand-written text

Why Use the Support Vector Machine Algorithm?

SVM algorithm offers various benefits such as:

  • Effective in separating non-linear data
  • Highly accurate in both lower and higher dimensional spaces
  • Immune to the overfitting problem as the support vectors only impact the position of the hyperplane.

Check out: 6 Types of Activation Function in Neural Networks You Need to Know

Summing Up

We have looked at the Support Vector Machine Algorithm in this article in detail. We learned about the SVM algorithm, how it works, its types, applications, benefits, and implementation in Python. This article will give you a basic idea about the SVM algorithm and answer some of your questions.

But it will also bring some other questions such as how the SVM algorithm knows which the right hyperplane is, what are other libraries available in Python, and where to find the training dataset? If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

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