{"id":167184,"date":"2023-03-10T10:13:24","date_gmt":"2023-03-10T09:13:24","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=167184"},"modified":"2023-10-30T18:43:34","modified_gmt":"2023-10-30T17:43:34","slug":"resampling-a-method-for-balancing-data","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/resampling-a-method-for-balancing-data","title":{"rendered":"Resampling: A method for balancing data"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"167184\" class=\"elementor elementor-167184\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-725c118 e-flex e-con-boxed e-con\" data-id=\"725c118\" 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-fe2bd1f elementor-widget elementor-widget-text-editor\" data-id=\"fe2bd1f\" 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><span style=\"font-weight: 400;\">Unbalanced data is very common in <\/span><b>Machine Learning<\/b><span style=\"font-weight: 400;\">. Unfortunately, they complicate predictive analysis. So to balance these data sets, several methods have been implemented.\u00a0<\/span><\/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-fad4a5b e-flex e-con-boxed e-con\" data-id=\"fad4a5b\" 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-99af8d7 elementor-widget elementor-widget-heading\" data-id=\"99af8d7\" 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><h3 class=\"elementor-heading-title elementor-size-default\">How to manage unbalanced data with resampling?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2289cf9 elementor-widget elementor-widget-text-editor\" data-id=\"2289cf9\" 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><span style=\"font-weight: 400;\">Unbalanced data are characterized by samples where a strong disparity is observed. For example, the ratio between classes is not 50\/50, but rather 90\/10. It is from this point on that data will pose problems in <\/span><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\"><b>Machine Learning<\/b><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\"><b>Deep Learning<\/b><\/a><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">This often refers to relatively rare events, such as insurance fraud or the detection of disease. For<\/span><b> example<\/b><span style=\"font-weight: 400;\">, in a population, a large majority may be<\/span><b> healthy<\/b><span style=\"font-weight: 400;\">, and only 0.1% may have multiple sclerosis. The healthy people are then the majority class and the sick people are considered the minority class. While these events are quite common, they represent only a<\/span><b> tiny fraction of the sample studied<\/b><span style=\"font-weight: 400;\">. Thai is why it could be difficult to predict them<\/span><\/p><p><span style=\"font-weight: 400;\">In Machine Learning, <\/span><b>unbalanced data<\/b><span style=\"font-weight: 400;\"> are very common, especially for binary classification. This is why several methods are being developed to better manage unbalanced classification problems. In particular through data resampling.<\/span><\/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-552909d e-flex e-con-boxed e-con\" data-id=\"552909d\" 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-e0b3c25 elementor-widget elementor-widget-heading\" data-id=\"e0b3c25\" 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\">What are the two resampling methods?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1cb2835 elementor-widget elementor-widget-text-editor\" data-id=\"1cb2835\" 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><span style=\"font-weight: 400;\">Resampling consists in modifying the data set before training the predictive model. This balances the data to <strong>make the prediction easier<\/strong>. For this purpose, there are two resampling methods.<\/span><\/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-a906203 elementor-widget elementor-widget-heading\" data-id=\"a906203\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Oversampling\n<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9cc8942 elementor-widget elementor-widget-text-editor\" data-id=\"9cc8942\" 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><span style=\"font-weight: 400;\">This involves <\/span><b>increasing the data<\/b><span style=\"font-weight: 400;\"> belonging to the minority class until reaching a certain balance. Or at least a satisfactory rate to make reliable predictions.<\/span><\/p><p><span style=\"font-weight: 400;\">Data Scientists can use two resampling methods. To wit:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Random oversampling<\/b><span style=\"font-weight: 400;\">: here, the minority data are cloned several times at random. This technique is especially useful for linear models, such as <\/span><b>logistic regression<\/b><span style=\"font-weight: 400;\">.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Synthetic oversampling<\/b><span style=\"font-weight: 400;\">: the idea is, again, to <\/span><b>add data from minority classes<\/b><span style=\"font-weight: 400;\">. But instead of copying them identically, the algorithm creates separate but similar data.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b4de7bb elementor-widget elementor-widget-heading\" data-id=\"b4de7bb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Subsampling<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5b9e7b2 elementor-widget elementor-widget-text-editor\" data-id=\"5b9e7b2\" 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><span style=\"font-weight: 400;\">On the contrary, undersampling will <\/span><b>decrease the data<\/b><span style=\"font-weight: 400;\"> from the majority class to balance the ratio.<\/span><\/p><p><b>Random undersampling is mainly used<\/b><span style=\"font-weight: 400;\">. This means that the majority of data are removed randomly. This resampling technique should be preferred when you have large data sets (at least several tens of thousands of cases).<\/span><\/p><p><span style=\"font-weight: 400;\">If this method is the most common, you can also use<\/span><b> undersampling<\/b><span style=\"font-weight: 400;\"> of border observations or clustering-based undersampling. That is to say, specifically remove certain majority data.<\/span><\/p><p><span style=\"font-weight: 400;\">In both cases, it is not necessary to obtain a perfect 50\/50 balance. It is possible to remove majority data or add minority data to obtain a ratio of 45\/55 or even 40\/60.<\/span><\/p><p><span style=\"font-weight: 400;\">In addition, do not hesitate to<\/span><b> test several class ratios<\/b><span style=\"font-weight: 400;\"> to select the one that will give you the best prediction performance.<\/span><\/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-cd59233 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"cd59233\" 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-md\" href=\"https:\/\/datascientest.com\/en\/processus-inscription-certifications\">\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 Data Science 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<div class=\"elementor-element elementor-element-1ab4d77 e-flex e-con-boxed e-con\" data-id=\"1ab4d77\" 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-4a49a2d elementor-widget elementor-widget-html\" data-id=\"4a49a2d\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"How to manage unbalanced data with resampling?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Unbalanced data are characterized by samples where a strong disparity is observed. 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Unbalanced data are characterized by samples where a strong disparity is observed. For example, the ratio between classes is not 50\/50, but rather 90\/10. [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":167186,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"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>Resampling: A method for balancing data<\/title>\n<meta name=\"description\" content=\"Unbalanced data is very common in Machine Learning. Unfortunately, they complicate predictive analysis. So to balance these data sets, several methods have been implemented. 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