{"id":178163,"date":"2024-03-01T15:12:31","date_gmt":"2024-03-01T14:12:31","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=178163"},"modified":"2024-02-11T18:44:29","modified_gmt":"2024-02-11T17:44:29","slug":"bagging-machine-learning-what-is-it-about","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/bagging-machine-learning-what-is-it-about","title":{"rendered":"Bagging Machine Learning: What is it about?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"178163\" class=\"elementor elementor-178163\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b46aca0 e-flex e-con-boxed e-con\" data-id=\"b46aca0\" 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<div class=\"elementor-element elementor-element-bf39119 e-con-full e-flex e-con\" data-id=\"bf39119\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5dda0e9 elementor-widget elementor-widget-heading\" data-id=\"5dda0e9\" 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\">Bagging Machine Learning: \"Together we're stronger\" - bagging could be symbolised by this quote. In fact, this technique is one of the ensemble methods, which consists of considering a set of models in order to make the final decision. Let's take a closer look at bagging.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7de508 elementor-widget elementor-widget-text-editor\" data-id=\"a7de508\" 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>First of all, we need to work on the data. We&#8217;re not going to provide all our models with the same data, because we want our models to be independent. The problem is that we can&#8217;t <a href=\"https:\/\/datascientest.com\/en\/what-is-a-dataset-how-do-i-work-with-it\">partition the dataset<\/a>. For a large number of models, our models would not be sufficiently trained and would <strong>produce mediocre results.<\/strong><\/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-ac6e82a elementor-widget elementor-widget-heading\" data-id=\"ac6e82a\" 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\">The solution to our problems: bootstrapping<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd9dad8 elementor-widget elementor-widget-text-editor\" data-id=\"cd9dad8\" 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>To overcome this problem, we \u00a0<a href=\"https:\/\/datascientest.com\/en\/what-is-a-dataset-how-do-i-work-with-it\">create a bootstrap dataset.<\/a> With this method, we create a new dataset from the initial dataset. The new dataset is the same size as the initial one. Let n be the sample size and let E_1 be the initial dataset and E_2 the <a href=\"https:\/\/datascientest.com\/en\/excel-to-power-bi-how-to-transform-a-pivot-table-in-excel-into-a-dataset-that-can-be-used-by-power-bi\">bootstrapped dataset<\/a>.<\/p><p>From E_1, we will randomly select an individual and place it in E_2. We will repeat this step until E_2 is of size n. It&#8217;s important to note that the elements chosen are always in E_1, so we can select the same element several times. It is this feature that allows us to <strong>produce different data.<\/strong><\/p><p>Let&#8217;s take a look at a concrete example, using the same notation as above.<\/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-e423cb0 elementor-widget elementor-widget-image\" data-id=\"e423cb0\" 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=\"800\" height=\"439\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/05\/illu_bagging-schema_2-1.png\" class=\"attachment-large size-large wp-image-78772\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/05\/illu_bagging-schema_2-1.png 1024w, https:\/\/datascientest.com\/en\/files\/2021\/05\/illu_bagging-schema_2-1-300x165.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/05\/illu_bagging-schema_2-1-768x422.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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-cc937dc elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"cc937dc\" 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\">Find out more about Data Scientist training<\/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<div class=\"elementor-element elementor-element-55bebcf elementor-widget elementor-widget-text-editor\" data-id=\"55bebcf\" 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<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><p>Here E_1 is made up of 2 elements called A and 2 elements called B, E_2 is empty and n=4.<\/p><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><p>On the first iteration, we randomly select an element from E_1 and place B in E_2.<\/p><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><p>This process is repeated until E_2 has 4 elements.<br \/>Finally, E_2 is made up of 3 elements B and one element A. An element B has been selected 3 times.<\/p><p>Now that we&#8217;ve <strong>seen what the bootstrap<\/strong> is, we decide to make 3 bootstrap samples*. For each sample, we&#8217;ll assign a different model. Decision trees are commonly used. We train each model and make our predictions.<\/p><p>*the choice of 3 is arbitrary<\/p><\/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-0f10272 elementor-widget elementor-widget-heading\" data-id=\"0f10272\" 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 can we do now with 3 predictions? <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5192261 elementor-widget elementor-widget-text-editor\" data-id=\"5192261\" 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>We need a single value for our problem, <a href=\"https:\/\/datascientest.com\/en\/distributed-architecture-definition-and-relationship-to-big-data\">and that&#8217;s where we do data aggregation.<\/a> For<a href=\"https:\/\/datascientest.com\/en\/classification-algorithms-definition-and-main-models\"> classification models<\/a>, we will apply a voting system and for regression models, we will average the predicted value. Referring to the initial quote, we build a final model from several models.<\/p><p>We can see an example of a voting system where we have to <a href=\"https:\/\/datascientest.com\/en\/predictive-elevator-maintenance-challenges-and-technology\">predict A or B,<\/a> with the example below:<\/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-e3ee8b7 elementor-widget elementor-widget-image\" data-id=\"e3ee8b7\" 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=\"800\" height=\"439\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/05\/illu_bagging-schema3.png\" class=\"attachment-large size-large wp-image-78773\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/05\/illu_bagging-schema3.png 1024w, https:\/\/datascientest.com\/en\/files\/2021\/05\/illu_bagging-schema3-300x165.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/05\/illu_bagging-schema3-768x422.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a6ec3be e-flex e-con-boxed e-con\" data-id=\"a6ec3be\" 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<div class=\"elementor-element elementor-element-3f87926 e-con-full e-flex e-con\" data-id=\"3f87926\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-545a879 elementor-widget elementor-widget-text-editor\" data-id=\"545a879\" 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>To understand what bagging is, we can remember that it is a combination of bootstrapping and aggregating. A famous bagging algorithm is the random forest, which you can find here. You can also find out more about another <a href=\"https:\/\/datascientest.com\/en\/catboost-an-essential-machine-learning-tool\">well-known aggregation technique, boosting, in this article.<\/a><\/p><p><strong>Bagging = Bootstrap + aggregating<\/strong><\/p><p>One of the advantages of this method is that it can reduce variance. Even if the models are not trained on the same data set, the bootstrap samples <strong>share common tuples,<\/strong> which produces bias. This bias reduces the variance.<\/p><p>A second advantage is to correct prediction errors. Let&#8217;s look at the image below. Each model makes a <a href=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-i\">classification prediction error,<\/a> but each error is corrected by the voting system.<\/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-d5ad017 elementor-widget elementor-widget-image\" data-id=\"d5ad017\" 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=\"800\" height=\"439\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/06\/illu_bagging_schema3-24.png\" class=\"attachment-large size-large wp-image-78841\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/06\/illu_bagging_schema3-24.png 1024w, https:\/\/datascientest.com\/en\/files\/2021\/06\/illu_bagging_schema3-24-300x165.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/06\/illu_bagging_schema3-24-768x422.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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-6cb3e9d elementor-widget elementor-widget-text-editor\" data-id=\"6cb3e9d\" 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>We have seen how\u00a0<strong>Bagging in Machine Learning<\/strong> fits in with ensemble techniques. As stated in the introduction, we use several models whose predictions we aggregate to obtain a final prediction. To calibrate our different models, we create different data sets by bootstrapping on the initial sample.<\/p><p>If you want to put this into practice, don&#8217;t hesitate to sign up for our <a href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">Data Scientist training course.<\/a><\/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-3c8a8b0 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"3c8a8b0\" 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-scientist-course\">\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 the Data Scientist course<\/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<\/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>Bagging Machine Learning: &#8220;Together we&#8217;re stronger&#8221; &#8211; bagging could be symbolised by this quote. In fact, this technique is one of the ensemble methods, which consists of considering a set of models in order to make the final decision. Let&#8217;s take a closer look at bagging. First of all, we need to work on the [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":178166,"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>Bagging Machine Learning: What is it about?<\/title>\n<meta name=\"description\" content=\"Bagging Machine Learning: &quot;Together we&#039;re stronger&quot; - bagging could be symbolised by this quote. 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