{"id":172122,"date":"2023-10-16T08:01:47","date_gmt":"2023-10-16T07:01:47","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=172122"},"modified":"2023-10-16T08:06:02","modified_gmt":"2023-10-16T07:06:02","slug":"svm-support-vector-machines-unveiled-understanding-implementation-significance","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/svm-support-vector-machines-unveiled-understanding-implementation-significance","title":{"rendered":"SVM (Support Vector Machines) Unveiled: Understanding, Implementation, Significance"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"172122\" class=\"elementor elementor-172122\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6333de76 e-flex e-con-boxed e-con\" data-id=\"6333de76\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4002daf4 elementor-widget elementor-widget-heading\" data-id=\"4002daf4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Welcome to the first part of this mini-series dedicated to SVM. <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6e63d55d e-flex e-con-boxed e-con\" data-id=\"6e63d55d\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6ecae2c0 elementor-widget elementor-widget-text-editor\" data-id=\"6ecae2c0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>When we think of <a href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-all-about-the-job\">Machine Learning,<\/a> we generally think of recent methods that are still poorly understood and used empirically, like a sorcerer&#8217;s apprentice.<\/p><p>Indeed, genetic algorithms, <a href=\"https:\/\/datascientest.com\/en\/convolutional-neural-network-everything-you-need-to-know\">neural networks,<\/a> boosting&#8230; these algorithms are often described as &#8220;black boxes&#8221;. In other words, while it&#8217;s easy to comment on the input and output of the algorithm independently, when it comes to detailing the exact contribution of each input to the final result, it&#8217;s a different kettle of fish. Blame it on too many parameters, the use of random events&#8230;<\/p><p>Nevertheless, among this <a href=\"https:\/\/datascientest.com\/en\/algorithm-what-is-it\">myriad of algorithms,<\/a> each more complex than the last, there is one whose advanced age belies its effectiveness and simplicity.<\/p><p>Conceptualized by Vladimir Vapnik in the early 60s, it entered the computer world in 1990. Since then, it has become a reference for all those interested in supervised learning techniques. Even today, it continues to be taught, studied and widely used. As you may have gathered, we&#8217;re talking about <strong>Support Vector Machines (SVM).<\/strong><\/p><p><strong>\u26a0\ufe0fWarning:<\/strong> talking about the simplicity of such an algorithm is like talking about the smallness of the Moon. We know it&#8217;s small compared with its surroundings, but we couldn&#8217;t possibly walk around it.<\/p><p>This article will be divided into 2 parts. In the first, we&#8217;ll introduce you to the basic principle of Margin Classifiers, and their limitations.<\/p><p>In the second part, we&#8217;ll go into more detail about SVMs, with the aim of providing an overview of how they work.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-32650136 e-flex e-con-boxed e-con\" data-id=\"32650136\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3ca4b59e elementor-widget elementor-widget-heading\" data-id=\"3ca4b59e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Maximal Margin Classifiers<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-55b058d5 elementor-widget elementor-widget-text-editor\" data-id=\"55b058d5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>As is often the case in science, before tackling the problem as such, we prefer to study a particular case, which is simpler to solve. So we&#8217;ll begin this presentation with a study of a cousin of <strong>SVMs, Maximal Margin Classifiers.<\/strong><\/p><p>Suppose we have a dataset containing 2 types of observations: &#8220;+&#8221; and &#8220;-&#8220;. Our objective is to be able to predict the class (&#8220;+&#8221; or &#8220;-&#8220;) of a new observation, represented here by an &#8220;x&#8221;.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7aa78fb2 elementor-widget elementor-widget-image\" data-id=\"7aa78fb2\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"349\" height=\"231\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm.png\" class=\"attachment-full size-full wp-image-172126\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm.png 349w, https:\/\/datascientest.com\/en\/files\/2023\/10\/svm-300x199.png 300w\" sizes=\"(max-width: 349px) 100vw, 349px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7e209eda elementor-widget elementor-widget-text-editor\" data-id=\"7e209eda\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Instinctively, we understand that the observation should be classified as a &#8220;-&#8221; here. But it&#8217;s difficult to explain the reasons why.<\/p><p>We&#8217;ll apply the <strong>following method:<\/strong><\/p><p>Draw a straight line separating the 2 classes. If the observation lies above this line, we&#8217;ll call it a &#8220;+&#8221;, otherwise a &#8220;-&#8220;. Here, for example, observation &#8220;x&#8221; is above the line, so it&#8217;s a &#8220;-&#8220;.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-28d5f261 elementor-widget elementor-widget-image\" data-id=\"28d5f261\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"349\" height=\"231\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm2.png\" class=\"attachment-full size-full wp-image-172127\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm2.png 349w, https:\/\/datascientest.com\/en\/files\/2023\/10\/svm2-300x199.png 300w\" sizes=\"(max-width: 349px) 100vw, 349px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2e44e892 elementor-widget elementor-widget-text-editor\" data-id=\"2e44e892\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The method seems to work well. However, in the particular case of our problem, there are an infinite number of straight lines perfectly separating the 2 sets.<\/p><p>Each of them delimits a different space and represents a different answer to the problem. The question is how to choose the best straight line from all the possible ones.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2234e052 elementor-widget elementor-widget-image\" data-id=\"2234e052\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"349\" height=\"231\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm3.png\" class=\"attachment-full size-full wp-image-172128\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm3.png 349w, https:\/\/datascientest.com\/en\/files\/2023\/10\/svm3-300x199.png 300w\" sizes=\"(max-width: 349px) 100vw, 349px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6cc7563d elementor-widget elementor-widget-text-editor\" data-id=\"6cc7563d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>On the previous figure, it seems logical to say that the central line separates the data better than the other 2. Indeed, as it is far from both the &#8220;+&#8221; and the &#8220;-&#8220;, it is unlikely to classify an observation in one class when it is actually very close to the other.<\/p><p>In <strong>mathematics, the distance separating a straight line from the nearest observation is called the margin.<\/strong> The central straight line has a larger margin than the other two.<\/p><p>In other words, the straight line that best separates the 2 classes is the one with the highest margin. Do you now understand where the term &#8220;Maximal Margin Classifiers&#8221; comes from?<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59264b83 elementor-widget elementor-widget-image\" data-id=\"59264b83\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"349\" height=\"231\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm4.png\" class=\"attachment-full size-full wp-image-172129\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm4.png 349w, https:\/\/datascientest.com\/en\/files\/2023\/10\/svm4-300x199.png 300w\" sizes=\"(max-width: 349px) 100vw, 349px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fbb9861 elementor-widget elementor-widget-text-editor\" data-id=\"3fbb9861\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In our example, the continuous line is the best separating line. The margin, shown as a dotted line, is maximum. You&#8217;ll have noticed that the same distance separates the straight line from the 3 nearest observations. This is a<strong> characteristic that is systematically found for the best straight line.<\/strong><\/p><p>The <strong>Maximal Margin Classifier<\/strong> algorithm uses this method to calculate the best straight line.<\/p><p>What&#8217;s important to remember from this introduction is that it&#8217;s enough to find the line with the largest margin to be <a href=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-ii\">able to classify all new observations.<\/a><\/p><p>What&#8217;s more, when it exists, it&#8217;s relatively simple to calculate the equation of the separation line (if you&#8217;re interested in the details of the calculations, I recommend the MIT OpenCourseWare video, which deals precisely with this subject .<\/p><p>This is a quick and easy way to answer the problem at hand.<\/p><p>However, the method we&#8217;ve just seen has two major shortcomings. It is highly sensitive to extreme values (also known as outliers).<\/p><p>The arrival of an outlier (the highest &#8220;-&#8220;) completely disrupts the result.<\/p><p>Worse still, in case you were wondering: No, there isn&#8217;t always a straight line separating the 2 classes perfectly. This is the case, for example, in the following image:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2a2e3995 elementor-widget elementor-widget-image\" data-id=\"2a2e3995\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"349\" height=\"231\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm6.png\" class=\"attachment-full size-full wp-image-172130\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/svm6.png 349w, https:\/\/datascientest.com\/en\/files\/2023\/10\/svm6-300x199.png 300w\" sizes=\"(max-width: 349px) 100vw, 349px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8a5a48a elementor-widget elementor-widget-text-editor\" data-id=\"8a5a48a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In the end, we end up with an algorithm that, in some cases, doesn&#8217;t work at all.<\/p><p>In the next article dedicated to SVMs, we&#8217;ll look at how we can give our algorithm more flexibility, by <a href=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-i\">allowing it to make a few classification errors, to improve its overall performance.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2e7aa7f2 e-flex e-con-boxed e-con\" data-id=\"2e7aa7f2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-40f2319a elementor-widget elementor-widget-text-editor\" data-id=\"40f2319a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Are you interested in Machine Learning techniques? Would you like to learn how to master them? Our data science training courses are just what you need!<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-48f5bbf5 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"48f5bbf5\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Welcome to the first part of this mini-series dedicated to SVM. When we think of Machine Learning, we generally think of recent methods that are still poorly understood and used empirically, like a sorcerer&#8217;s apprentice. Indeed, genetic algorithms, neural networks, boosting&#8230; these algorithms are often described as &#8220;black boxes&#8221;. In other words, while it&#8217;s easy [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":172124,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2409],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>SVM (Support Vector Machines) Unveiled: Understanding, Implementation, Significance<\/title>\n<meta name=\"description\" content=\"SVM, or Support Vector Machines, demystified: Understand what they are, how they work, and why they&#039;re essential in machine learning.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/svm-support-vector-machines-unveiled-understanding-implementation-significance\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SVM (Support Vector Machines) Unveiled: Understanding, Implementation, Significance\" \/>\n<meta property=\"og:description\" content=\"Welcome to the first part of this mini-series dedicated to SVM. When we think of Machine Learning, we generally think of recent methods that are still\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/en\/svm-support-vector-machines-unveiled-understanding-implementation-significance\" \/>\n<meta property=\"og:site_name\" content=\"Data Science Courses | DataScientest\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientestEN\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-16T07:01:47+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-10-16T07:06:02+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/SVM.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"562\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Melanie\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" 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