{"id":8186,"date":"2018-12-14T14:43:04","date_gmt":"2018-12-14T09:13:04","guid":{"rendered":"http:\/\/ivyproschool.com\/blog\/?p=8186"},"modified":"2021-06-22T19:05:55","modified_gmt":"2021-06-22T13:35:55","slug":"beginners-guide-to-support-vector-machines","status":"publish","type":"post","link":"https:\/\/ivyproschool.com\/blog\/beginners-guide-to-support-vector-machines\/","title":{"rendered":"Beginners Guide to Support Vector Machines"},"content":{"rendered":"<p><a href=\"https:\/\/ivyproschool.com\/blog\/beginners-guide-to-support-vector-machines\/\"><br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-8187\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/11-Companies-in-Pune-that-are-Hiring-Business-analysts-2.png\" alt=\"\" width=\"560\" height=\"315\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/11-Companies-in-Pune-that-are-Hiring-Business-analysts-2.png 560w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/11-Companies-in-Pune-that-are-Hiring-Business-analysts-2-300x169.png 300w\" sizes=\"auto, (max-width: 560px) 100vw, 560px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p>Support Vector Machine (SVM) is a supervised <a href=\"https:\/\/ivyproschool.com\/our-courses\/big-data-and-analytics\/data-science-machine-learning-certification\/\">Machine Learning<\/a> algorithm that performs classification tasks by constructing hyperplanes or classifiers in a multidimensional space that separates the data into two different classes. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables.<\/p>\n<p>In the diagram below you can see that there are two different classes of data( Blue and Red ). You can separate them with several separating lines such as A, B, C and\u00a0 D. But there must be a single separating line or hyperplane that classifies the two classes best. So, the question is: How to choose the ultimate classifier?<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-8192\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram1_SVM.png\" alt=\"\" width=\"573\" height=\"167\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram1_SVM.png 573w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram1_SVM-300x87.png 300w\" sizes=\"auto, (max-width: 573px) 100vw, 573px\" \/><\/p>\n<p><strong>GOOD VS BAD CLASSIFIERS<\/strong><\/p>\n<p><strong>\u00a0<\/strong>First it is necessary to know a <strong>few terms<\/strong>:<\/p>\n<p>Data that can be separated by a line or a simple hyperplane is known as\u00a0<em><strong>linearly separable\u00a0<\/strong><\/em><strong>data<\/strong>. Now those hyperplanes that can linearly separate the data are known as <strong>linear<em> classifiers<\/em><\/strong><em>.<\/em><\/p>\n<p>If the training data is\u00a0linearly separable, we can obtain two parallel hyperplanes that separate the two classes of data, so that the <strong>distance between them is maximized<\/strong>. The region bounded by these two hyperplanes is called the &#8220;<strong>margin<\/strong>&#8220;, and the <strong>maximum margin hyperplane<\/strong> is the hyperplane that lies halfway between them. In the diagram below the line labelled as <strong>separation line<\/strong> is the maximum margin hyperplane.<\/p>\n<p>The data points that lie closest to the hyperplanes are called <strong>Support vectors<\/strong>. They are the data points most difficult to classify because they lie at the boundaries of the margin and acts as a support to maximize the margins.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-8191\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram2_SVM.png\" alt=\"\" width=\"619\" height=\"227\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram2_SVM.png 619w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram2_SVM-300x110.png 300w\" sizes=\"auto, (max-width: 619px) 100vw, 619px\" \/><\/p>\n<p>In this example, we have two separate lines for the data. But, is there a good reason to choose one over another?<\/p>\n<p>This question can only be answered by the<strong> margins<\/strong>. From the diagram, it is very clear that the margin on the diagram of the right side is larger. So the hyperplane on the right side <strong>maximizes the distance between nearest data points<\/strong>. The aim of SVM is to choose the hyperplane in such a way that <strong>maximizes the margin between the two different classes<\/strong> of the dataset.<\/p>\n<p>We need to remember that a classifier is not worth it when the two classes seem to run too close to each other and when we run this same classifier through test data, there are <strong>high chances of misclassification<\/strong>. So the objective is to maximize the margin, minimize the risk involved in it and thus <strong>generalize<\/strong> it well on test data.<\/p>\n<p>So, from the above example, we can conclude that \u2018<strong>B<\/strong>\u2019 should be the ultimate separating line between the two classes. Now, let us take another situation. Look at the data points below; can you classify the data with a linear classifier without any misclassification?<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-8200\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram3_SVM1.png\" alt=\"\" width=\"818\" height=\"139\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram3_SVM1.png 818w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram3_SVM1-300x51.png 300w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram3_SVM1-768x131.png 768w\" sizes=\"auto, (max-width: 818px) 100vw, 818px\" \/><\/p>\n<p>No, it is not that easy. So instead of using a linear classifier, why don\u2019t we use a nonlinear separating line as below?<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-8199\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram4_SVM1.png\" alt=\"\" width=\"803\" height=\"182\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram4_SVM1.png 803w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram4_SVM1-300x68.png 300w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram4_SVM1-768x174.png 768w\" sizes=\"auto, (max-width: 803px) 100vw, 803px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p>The <strong>nonlinear classifier<\/strong> works very well to classify them and more importantly without any misclassifications.<\/p>\n<p>Now, earlier I mentioned in the introduction that the SVM algorithm works on classification problems in multidimensional space. These multiple or \u2018n\u2019 dimensions are nothing but the number of features in the data set. And the hyperplane just looks like a thin sheet of paper that separates the two classes in a similar way as the diagram below.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-8188\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram5_SVM.png\" alt=\"\" width=\"596\" height=\"143\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram5_SVM.png 596w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/Diagram5_SVM-300x72.png 300w\" sizes=\"auto, (max-width: 596px) 100vw, 596px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p>Ivy Pro School is India&#8217;s Top Ranked premier <a href=\"https:\/\/ivyproschool.com\/\">Data Science Institute<\/a> with courses in <a href=\"https:\/\/ivyproschool.com\/data-science-ml-ai-no-upfront-fee\/\" rel=\"noopener noreferrer\" target=\"_blank\">Machine Learning<\/a>,<a href=\"https:\/\/ivyproschool.com\/our-courses\/big-data-and-analytics\/machine-learning-python-certification\/\" rel=\"noopener noreferrer\" target=\"_blank\"> Python<\/a>, R, SAS, Hadoop, <a href=\"https:\/\/ivyproschool.com\/our-courses\/big-data-and-analytics\/data-science-advanced-tableau-certification-course\/\" rel=\"noopener noreferrer\" target=\"_blank\">Tableau<\/a> and other latest technologies in <a href=\"https:\/\/ivyproschool.com\/our-courses\/big-data-and-analytics\/data-science-machine-learning-artificial-intelligence-deep-learning-certification-course\/\" rel=\"noopener noreferrer\" target=\"_blank\">Data Science<\/a>. Call us at 9748441111 for more information.<\/p>\n<p>This article has been contributed by our student Saptarshi from Kolkata.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; Support Vector Machine (SVM) is a supervised Machine Learning algorithm that performs classification tasks by constructing hyperplanes or classifiers in a multidimensional space that separates the data into two different classes. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables. In the diagram below you can see that there are two different classes of data( Blue and Red ). You can separate them with several separating lines such as A, B, C and\u00a0 D. But there must be a single separating line or hyperplane that classifies the two classes best. So, the question is: How to choose the ultimate classifier? GOOD VS BAD CLASSIFIERS \u00a0First it is necessary to know a few terms: Data that can be separated by a line or a simple hyperplane is known as\u00a0linearly separable\u00a0data. Now those hyperplanes that can linearly separate the data are known as linear classifiers. If the training data is\u00a0linearly separable, we can obtain two parallel hyperplanes that separate the two classes of data, so that the distance between them is maximized. The region bounded by these two hyperplanes is called the &#8220;margin&#8220;, and the maximum margin hyperplane is the hyperplane that lies halfway between [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":8218,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[851,5,881,715],"tags":[22,76,91,467,728,504],"class_list":["post-8186","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-analytics","category-data-analytics","category-data-science","category-machine-learning-ai","tag-analytics","tag-business-analytics","tag-data-analyst","tag-data-science","tag-data-science-career","tag-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Beginner&#039;s Guide to Support Vector Machines | Ivy Pro School<\/title>\n<meta name=\"description\" content=\"In this blog, We will discuss about the Beginner&#039;s Guide to support vector machines. 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