{"id":19730,"date":"2020-09-02T16:52:42","date_gmt":"2020-09-02T11:22:42","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/support-vector-regression\/"},"modified":"2024-09-02T15:30:54","modified_gmt":"2024-09-02T10:00:54","slug":"support-vector-regression","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/support-vector-regression\/","title":{"rendered":"Support Vector Regression in Machine Learning"},"content":{"rendered":"\n

Supervised Machine Learning Models with associated learning algorithms that analyze data for classification and regression analysis are known as Support Vector Regression. SVR is built based on the concept of Support Vector Machine<\/a> or SVM. It is one among the popular Machine Learning models that can be used in classification problems or assigning classes when the data is not linearly separable.<\/p>\n\n\n\n

Contributed by: Vijay Krishnan MR<\/a> <\/p>\n\n\n\n

Introduction to Support Vector Regression<\/strong><\/h2>\n\n\n\n

Before we dive into the topic of Support vector Regression (SVR), it is important to know the concept of SVM based on which SVR is built.<\/p>\n\n\n\n

It is one of the classic examples of supervised Machine learning<\/a> technique. We could say it\u2019s one of the more powerful models which can be used in classification problems or assigning classes when the data is not linearly separable. I would give a classic kitchen example; I am sure most of us love chips? Of course, I do. I wanted to make homemade chips. I bought potatoes from the vegetable market and hit my kitchen. All I did was follow a YouTube video. I started slicing the potatoes in the hot oil, the result was a disaster, and I ended up getting chips which were dark brown\/black. <\/p>\n\n\n\n

What was wrong here? I had purchased potatoes, followed the procedure shown in the video and also maintained the right temperature of the oil, did I miss something? When I did my research by asking experts, I found that I did miss the trick, I had chosen potatoes with higher starch content instead of lower ones. All the potatoes looked the same for me, this is where the years and years of training comes into the picture, the experts had well-trained eyes on how to choose potatoes with lower starch. It has some specific features such as these potatoes would look fresh and will have some additional skin which could be peeled from our fingernails, and they look muddy.<\/p>\n\n\n\n

With the chips example, I was only trying to tell you about the nonlinear dataset. While many classifiers exist that can classify linearly separable data like logistic regression or linear regression, SVMs can handle highly non-linear data using an amazing technique called kernel trick. It implicitly maps the input vectors to higher dimensional (adds more dimensions) feature spaces by the transformation which rearranges the dataset in such a way that it is linearly solvable.<\/p>\n\n\n

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In short, a kernel is a function which places a low dimensional plane to a higher dimensional space where it can be segmented using a plane. In other words, it transforms linearly inseparable data to separable data by adding more dimensions to it.<\/p>\n\n\n\n

There are three kernels which SVM uses the most<\/strong><\/p>\n\n\n\n