{"id":8013,"date":"2020-12-29T00:26:24","date_gmt":"2020-12-29T00:26:24","guid":{"rendered":"https:\/\/healinglifespan.com\/data-science\/2020\/12\/29\/seven-most-popular-svm-kernels\/"},"modified":"2020-12-29T00:26:24","modified_gmt":"2020-12-29T00:26:24","slug":"seven-most-popular-svm-kernels","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2020\/12\/29\/seven-most-popular-svm-kernels\/","title":{"rendered":"Seven Most Popular SVM Kernels"},"content":{"rendered":"
\n
\"Support\"Support<\/span><\/div>\n
\n

\u00a0 \u00a0 While explaining the support vector machine<\/strong><\/a>, SVM algorithm, we said we have various svm kernel functions that help changing the data dimensions.<\/p>\n

So In this article, we are going to dive deep into svm algorithm and SVM\u2019s kernel functions. <\/p>\n

Let me give you a quick introduction of svm<\/strong><\/a> and its kernels.\u00a0<\/p>\n

We already know that SVM is a supervised machine learning algorithm used to deal with both classification and regression problems<\/strong><\/a>. Compared to the other classification and regression algorithms, the svm approach is completely different.\u00a0<\/p>\n

One key reason for this is svm kernel<\/strong> functions.<\/p>\n<\/div>\n

\n
\n
\n

Learn about the most popular SVM kernels along with the implementation in python #svm #svmkernels #classification #regression #machinelearning #datascience #python<\/p>\n<\/p><\/div>\n

\n\t\t\t
\n\t\t\t\t<\/i>
\n\t\t\t\tClick to Tweet<\/span>
\n\t\t\t<\/span>\n\t\t<\/p>\n<\/p><\/div>\n<\/div>\n

\n

Kernel plays a vital role in classification and is used to analyze some patterns in the given dataset. They are very helpful in solving a no-linear problem<\/strong> by using a linear classifier<\/strong><\/a>.\u00a0<\/p>\n

Later the svm algorithm uses kernel-trick<\/strong> for transforming the data points and creating an optimal decision boundary. Kernels help us to deal with high dimensional<\/strong> data in a very efficient manner.\u00a0<\/p>\n

We have various svm kernel functions to convert the non-linear data to linear. In this article, we listed 7 such popular svm kernel functions.<\/p>\n

Before we drive further, let\u2019s have a look at the topics you are going to learn in this article.<\/p>\n<\/div>\n

\n

Let\u2019s start the article with SVM. If you are interested in the sum algorithm implementation in python and R programming language, please refer to below two articles.<\/p>\n

What Is the Support Vector Machine (SVM)?<\/h2>\n<\/div>\n
\"What\"What<\/span><\/div>\n
\n

SVM is a famous supervised machine learning algorithm<\/strong><\/a> used for\u00a0<\/strong>classification as well as\u00a0<\/strong>regression algorithm<\/strong>s.<\/strong><\/a> However, \u00a0mostly it is preferred for classification algorithms. It basically separates different target classes in a hyperplane in n-dimensional<\/strong> or multidimensional space.<\/p>\n

The main motive of the SVM is to create the best decision boundary<\/strong> that can separate two or more classes(with maximum margin<\/strong>) so that we can correctly put new data points in the correct class.\u00a0<\/p>\n

Wait! <\/p>\n

Why is it known as SVM? <\/strong><\/p><\/blockquote>\n

Because It chooses extreme vectors or support vectors<\/strong> to create the hyperplane, that\u2019s why it is named so. In the below sections let’s understand in more detail.<\/p>\n

SVM Algorithm Explanation<\/h2>\n

To understand the SVM algorithm, we need to know about hyperplanes<\/strong> and support vectors<\/strong>.<\/p>\n<\/div>\n

\"Support\"Support<\/span><\/div>\n
\n

SVM Hyperplane<\/h3>\n

There may be multiple<\/strong> lines\/decision boundaries to segregate the classes in n-dimensional space. Still, we want to search out the simplest decision<\/strong> boundary that helps to classify the information points.\u00a0<\/p>\n

This best boundary is considered to be the hyperplane of SVM<\/strong>. The dimensions of the hyperplane rely on the features present within the dataset. These features suggest if there are 2 target labels in the dataset.\u00a0<\/p>\n

Then the hyperplane is going to be a line. And if there are 3 target labels, then the hyperplane is going to be a 2-dimension plane. We always create a hyperplane that provides maximum margin<\/strong>. This margin simply means there should be a maximum distance<\/strong> between the data points.<\/p>\n

SVM Support Vectors<\/h3>\n

Support vectors are defined as the data points, which are closest to the hyperplane and have some effect on its position. \u00a0As these vectors are supporting the hyperplane, therefore named as Support vectors.<\/p>\n

I guess now it\u2019s clear what svm is. Let’s use this understanding and pick an example to learn how the svm algorithm works.<\/p>\n

How the SVM Algorithm Works<\/h2>\n

As discussed, it mainly focuses on creating a hyperplane to separate target classes. Let me explain this by using a particular problem.\u00a0<\/p>\n

Suppose you are having the dataset as shown below in the image. Now, you have to classify the target classes.\u00a0<\/p>\n