{"id":192684,"date":"2025-01-29T06:30:00","date_gmt":"2025-01-29T05:30:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=192684"},"modified":"2025-01-26T18:20:01","modified_gmt":"2025-01-26T17:20:01","slug":"bagging-vs-boosting","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/bagging-vs-boosting","title":{"rendered":"Bagging vs Boosting: What is the Difference?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"192684\" class=\"elementor elementor-192684\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a0f0df9 e-flex e-con-boxed e-con\" data-id=\"a0f0df9\" 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-d9732f1 elementor-widget elementor-widget-text-editor\" data-id=\"d9732f1\" 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><b>When the performance of a single model falters in delivering accurate predictions, ensemble learning techniques often emerge as the preferred solution. The most renowned methods, Bagging (Bootstrap Aggregating) and Boosting, aim to enhance the accuracy of predictions in machine learning by amalgamating the outcomes from individual models to derive more robust and precise final predictions.<\/b><\/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-87cdaf2 e-flex e-con-boxed e-con\" data-id=\"87cdaf2\" 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-8d4021b elementor-widget elementor-widget-heading\" data-id=\"8d4021b\" 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\">Bagging: The Power of Parallel Learning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5be56b3 elementor-widget elementor-widget-image\" data-id=\"5be56b3\" 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\t\t\t<img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-1.webp\" class=\"attachment-full size-full wp-image-192686\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-1.webp 1920w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-1-300x169.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-1-1024x576.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-1-768x432.webp 768w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-1-1536x864.webp 1536w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/>\t\t\t\t\t\t\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-2b14d18 elementor-widget elementor-widget-text-editor\" data-id=\"2b14d18\" 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;\"><a href=\"https:\/\/datascientest.com\/en\/bagging-machine-learning-what-is-it-about\">Introduced by Leo Breiman in 1994<\/a>, Bagging involves training multiple versions of a predictor, such as a decision tree, which are trained <\/span><b>in parallel<\/b><span style=\"font-weight: 400;\"> and <\/span><b>independently<\/b><span style=\"font-weight: 400;\">. The first step in bagging is to perform a <\/span><b>random sampling with replacement<\/b><span style=\"font-weight: 400;\"> (known as <a href=\"https:\/\/en.wikipedia.org\/wiki\/Bootstrapping_(statistics)\">bootstrapping<\/a>) from the training dataset. Each predictor is assigned a training sample to make predictions, which are then combined with those from other distinct predictors. The final step involves calculating the average of predictions made by different models (for quantitative predictions) or using a voting method (for categorical predictions), <\/span><b>where the majority prediction based on the number of occurrences or probability is retained<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">The main strength of bagging lies in its ability to <\/span><b>reduce variance without increasing bias<\/b><span style=\"font-weight: 400;\">. By training models on different subsets that share a certain percentage of data, each model captures the diversity within the random datasets, leading to final results generalizing well on the test dataset. A real-world analogy is consulting several experts on a complex issue. Each expert, although competent, might have slightly different experiences and viewpoints. Averaging their opinions often results in <\/span><b>better decisions<\/b><span style=\"font-weight: 400;\"> than relying on a single expert.<\/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-57107a9 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"57107a9\" 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\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-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\">Follow a course in Machine Learning<\/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-1c10561 elementor-widget elementor-widget-text-editor\" data-id=\"1c10561\" 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;\">In summary,<\/span><b> aggregating several high-variance models<\/b><span style=\"font-weight: 400;\"> effectively captures the variations in each training set. This approach helps <\/span><b>smooth out the individual prediction errors<\/b><span style=\"font-weight: 400;\"> from different models to build a global model with <\/span><b>low variance<\/b><span style=\"font-weight: 400;\"> by combining <a href=\"https:\/\/datascientest.com\/en\/overfitting-what-is-it-how-can-i-avoid-it\">the predictions of several models with high variances (overfitting)<\/a>. Bagging has gained popularity through Random Forests, derived from parallel training of decision trees, known for their high variance.<\/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-3da25d0 e-flex e-con-boxed e-con\" data-id=\"3da25d0\" 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-9ee192d elementor-widget elementor-widget-heading\" data-id=\"9ee192d\" 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\">Boosting: Sequential Learning for Error Reduction<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-64de59c elementor-widget elementor-widget-text-editor\" data-id=\"64de59c\" 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;\">Unlike <\/span><b>Bagging<\/b><span style=\"font-weight: 400;\">, <\/span><b>Boosting<\/b><span style=\"font-weight: 400;\"> employs a <\/span><b>sequential<\/b><span style=\"font-weight: 400;\"> method in constructing the final model. Individual predictors are considered <\/span><b>weak<\/b><span style=\"font-weight: 400;\"> (underfitting) and are constructed in <\/span><b>series<\/b><span style=\"font-weight: 400;\">, one following another. Each model attempts to rectify the errors of its predecessor, thereby reducing the bias introduced by each weak model. Boosting algorithms include notable examples like <strong>AdaBoost<\/strong> (Adaptive Boosting), <\/span><b>Gradient Boosting<\/b><span style=\"font-weight: 400;\">, and its variants <\/span><a href=\"https:\/\/datascientest.com\/en\/xgboost-the-champion-of-competitive-machine-learning\">XGBoost<\/a><span style=\"font-weight: 400;\"> and <\/span><strong>LightGBM<\/strong><span style=\"font-weight: 400;\">.<\/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-ee297bd elementor-widget elementor-widget-image\" data-id=\"ee297bd\" 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\t\t\t<img decoding=\"async\" width=\"1200\" height=\"686\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-4.webp\" class=\"attachment-full size-full wp-image-192687\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-4.webp 1200w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-4-300x172.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-4-1024x585.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-4-768x439.webp 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/>\t\t\t\t\t\t\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-34f61c9 elementor-widget elementor-widget-text-editor\" data-id=\"34f61c9\" 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;\">The process starts with a weak learner making predictions on the training dataset. The <\/span><b>incorrectly predicted instances<\/b><span style=\"font-weight: 400;\"> are identified by the Boosting algorithm, which assigns them <\/span><b>higher weights<\/b><span style=\"font-weight: 400;\">. The next model concentrates more on these previously hard-to-classify cases during its training to enhance its predictions. This process continues, with each subsequent model addressing errors from previous weak learners, until the series&#8217; final model is trained. As with <\/span><b>Bagging<\/b><span style=\"font-weight: 400;\">, the number of models to be trained for the final predictions can be determined empirically, considering complexity, training time, and accuracy of final predictions.<\/span><\/p><p><b>Gradient Boosting<\/b><span style=\"font-weight: 400;\"> extends the concept of <\/span><b>Boosting<\/b><span style=\"font-weight: 400;\"> by employing a gradient-based approach to refine predictions. Each new model is trained to correct <\/span><b>the residuals of previous predictions<\/b><span style=\"font-weight: 400;\"> by following the loss function gradient&#8217;s direction, facilitating more precise and efficient optimization.<\/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-771fc8d elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"771fc8d\" 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\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-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\">Become an expert in Gradient Boosting<\/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-5456fb3 e-flex e-con-boxed e-con\" data-id=\"5456fb3\" 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-1920616 elementor-widget elementor-widget-heading\" data-id=\"1920616\" 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\">Key Differences and Trade-offs<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5682d01 elementor-widget elementor-widget-heading\" data-id=\"5682d01\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><font size=\"4\">1. Training Approach:<\/font><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c87b082 elementor-widget elementor-widget-text-editor\" data-id=\"c87b082\" 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><span style=\"font-weight: 400;\">Bagging: Models are trained <\/span><b>independently<\/b><span style=\"font-weight: 400;\"> and <\/span><b>in parallel<\/b><span style=\"font-weight: 400;\">.<\/span><\/li><li><span style=\"font-weight: 400;\">Boosting: Models are trained <\/span><b>sequentially<\/b><span style=\"font-weight: 400;\">, learning from the errors of the preceding ones.<\/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-d6baab8 elementor-widget elementor-widget-heading\" data-id=\"d6baab8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><font size=\"4\">2. Error Management:<\/font><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-252ee5b elementor-widget elementor-widget-text-editor\" data-id=\"252ee5b\" 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\"><span style=\"font-weight: 400;\">Bagging: Reduces variance through averaging.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Boosting: Reduces both bias and variance via sequential learning.<\/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-7b9f483 elementor-widget elementor-widget-image\" data-id=\"7b9f483\" 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\t\t\t<img decoding=\"async\" width=\"1200\" height=\"686\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-2.webp\" class=\"attachment-full size-full wp-image-192689\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-2.webp 1200w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-2-300x172.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-2-1024x585.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-2-768x439.webp 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/>\t\t\t\t\t\t\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-cfad43e elementor-widget elementor-widget-heading\" data-id=\"cfad43e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><font size=\"4\">3. Risk of Overfitting:<\/font><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-02f8a48 elementor-widget elementor-widget-text-editor\" data-id=\"02f8a48\" 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\"><span style=\"font-weight: 400;\">Bagging: Generally more robust against <\/span><b>overfitting<\/b><span style=\"font-weight: 400;\">.<\/span><\/li><li><span style=\"font-weight: 400;\">Boosting: More prone to <\/span><b>overfitting<\/b><span style=\"font-weight: 400;\">, especially when classifying <\/span><b>noisy<\/b><span style=\"font-weight: 400;\"> 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-80cee1d elementor-widget elementor-widget-heading\" data-id=\"80cee1d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><font size=\"4\">4. Training Speed:<\/font><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e0ddbb elementor-widget elementor-widget-text-editor\" data-id=\"3e0ddbb\" 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\"><span style=\"font-weight: 400;\">Bagging: Faster due to the parallel training of models.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Boosting: Slower, owing to its sequential nature.<\/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-fb8b2fb elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"fb8b2fb\" 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\" href=\"https:\/\/datascientest.com\/en\/data-analyst-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\">Master Data Analysis<\/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-364fc8e e-flex e-con-boxed e-con\" data-id=\"364fc8e\" 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-25a1353 elementor-widget elementor-widget-heading\" data-id=\"25a1353\" 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\">Practical Applications<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4cde103 elementor-widget elementor-widget-text-editor\" data-id=\"4cde103\" 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;\">Both techniques excel in different contexts. Bagging often performs well when:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Base models are complex (high variance).<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The dataset is noisy.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Parallel processing power is available.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Interpretability is a focus.<\/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-06fce51 elementor-widget elementor-widget-text-editor\" data-id=\"06fce51\" 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;\">Boosting typically excels when:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Base models are simple (high bias).<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The data is relatively free of noise.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Maximizing prediction accuracy is essential.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Computational resources can support sequential processing.<\/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-2ee126b elementor-widget elementor-widget-image\" data-id=\"2ee126b\" 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\t\t\t<img decoding=\"async\" width=\"1200\" height=\"686\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-3.webp\" class=\"attachment-full size-full wp-image-192688\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-3.webp 1200w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-3-300x172.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-3-1024x585.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/01\/bagging-vs-boosting-datascientest-3-768x439.webp 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/>\t\t\t\t\t\t\t\t\t\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-91e47cc e-flex e-con-boxed e-con\" data-id=\"91e47cc\" 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-780683e elementor-widget elementor-widget-heading\" data-id=\"780683e\" 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\">Implementation Considerations<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ebb0b71 elementor-widget elementor-widget-text-editor\" data-id=\"ebb0b71\" 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;\">When implementing these techniques, several factors should be considered:<\/span><\/p><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Size of the <\/b><a href=\"https:\/\/datascientest.com\/en\/what-is-a-dataset-how-do-i-work-with-it\"><b>Dataset<\/b><\/a><span style=\"font-weight: 400;\">: Large datasets tend to benefit more from bagging.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/datascientest.com\/en\/computational-resources-definition-operation-and-role\"><b>Computational Resources<\/b><\/a><span style=\"font-weight: 400;\">: Bagging can take advantage of parallel processing.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Parameter Tuning<\/b><span style=\"font-weight: 400;\">: Boosting usually requires more careful tuning.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Interpretability<\/b><span style=\"font-weight: 400;\">: Bagged models tend to be more interpretable.<\/span><\/li><\/ol>\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-9170449 e-flex e-con-boxed e-con\" data-id=\"9170449\" 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-74a9919 elementor-widget elementor-widget-heading\" data-id=\"74a9919\" 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\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-42f2625 elementor-widget elementor-widget-text-editor\" data-id=\"42f2625\" 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><b>Bagging<\/b><span style=\"font-weight: 400;\"> and <\/span><b>Boosting<\/b><span style=\"font-weight: 400;\"> have become fundamental techniques in machine learning. While Bagging offers <\/span><b>robustness and simplicity<\/b><span style=\"font-weight: 400;\"> through parallel learning, <\/span><b>Boosting<\/b><span style=\"font-weight: 400;\"> provides powerful <\/span><b>sequential improvements<\/b><span style=\"font-weight: 400;\">. Knowing their respective strengths and weaknesses helps practitioners choose the most suitable approach for their specific applications.<\/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-f554ab1 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"f554ab1\" 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\" 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<div class=\"elementor-element elementor-element-c08e58c e-flex e-con-boxed e-con\" data-id=\"c08e58c\" 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-29f9411 elementor-widget elementor-widget-html\" data-id=\"29f9411\" 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    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What is bagging in machine learning?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Bagging (Bootstrap Aggregating) is an ensemble technique in machine learning that involves training multiple models on random samples (with replacement) from the same dataset. The predictions of these models are then combined, typically through averaging or majority voting, to improve stability and reduce variance.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What is boosting in machine learning?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Boosting is an ensemble technique that combines multiple weak models by training them sequentially, where each model corrects the errors of the previous ones. Models are weighted based on their performance, and boosting aims to reduce bias and improve overall accuracy.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What is the main difference between bagging and boosting?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"The main difference lies in the training process. Bagging trains models in parallel on random samples to reduce variance, while boosting trains models sequentially to reduce bias by correcting errors from previous models.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"When should you use bagging or boosting?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Bagging is recommended when the base model is unstable and prone to high variance (such as decision trees). Boosting is ideal when the problem requires bias reduction and weak models can be combined to improve accuracy.\"\r\n      }\r\n    }\r\n  ]\r\n}\r\n<\/script>\r\n\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>When the performance of a single model falters in delivering accurate predictions, ensemble learning techniques often emerge as the preferred solution. The most renowned methods, Bagging (Bootstrap Aggregating) and Boosting, aim to enhance the accuracy of predictions in machine learning by amalgamating the outcomes from individual models to derive more robust and precise final predictions. [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":192686,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427,2425],"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 vs Boosting: What is the Difference?<\/title>\n<meta name=\"description\" content=\"Discover the differences between Bagging and Boosting, two key ensemble learning techniques, and learn how to use them to improve the performance of your AI models.\" \/>\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\/bagging-vs-boosting\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" 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