{"id":179819,"date":"2024-05-15T16:43:51","date_gmt":"2024-05-15T15:43:51","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=179819"},"modified":"2024-02-25T17:52:25","modified_gmt":"2024-02-25T16:52:25","slug":"the-power-of-ensemble-methods-in-data-science","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/the-power-of-ensemble-methods-in-data-science","title":{"rendered":"The Power of Ensemble Methods in Data Science"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"179819\" class=\"elementor elementor-179819\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5cffe07 e-flex e-con-boxed e-con\" data-id=\"5cffe07\" 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-cbb0682 elementor-widget elementor-widget-heading\" data-id=\"cbb0682\" 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\">Thanks to the development of Big Data and Artificial Intelligence, Data Scientists can use data learning models to perform predictive analyses. Among the most effective methods is ensembling.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aa02654 elementor-widget elementor-widget-text-editor\" data-id=\"aa02654\" 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>So what is it all about? How does model assembly work? What are its advantages and disadvantages? That&#8217;s what we&#8217;re going to find out in this article.<\/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-969b553 e-flex e-con-boxed e-con\" data-id=\"969b553\" 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-e50ad44 elementor-widget elementor-widget-heading\" data-id=\"e50ad44\" 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 is ensembling?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e915317 elementor-widget elementor-widget-text-editor\" data-id=\"e915317\" 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><strong>Ensemblin<\/strong>g combines several <a href=\"https:\/\/datascientest.com\/en\/deep-neural-network-what-is-it-and-how-is-it-working\">neural network models<\/a> to solve a given problem. Each model uses its own learning techniques. This enriches predictions.<\/p><p>With model<strong> ensemble<\/strong> learning, it is possible to obtain much more reliable and relevant results than with any single model.<\/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-260fdd8 e-flex e-con-boxed e-con\" data-id=\"260fdd8\" 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-752ea36 elementor-widget elementor-widget-heading\" data-id=\"752ea36\" 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\">How does model ensembling work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c55d188 elementor-widget elementor-widget-text-editor\" data-id=\"c55d188\" 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>Any <strong>learning model<\/strong> <a href=\"https:\/\/datascientest.com\/en\/adversarial-training-what-you-didnt-know-yet\">requires the use of large quantities of data<\/a> to produce relevant results. However, depending on the technique used, care must be taken in selecting the right components (share of bias and variance).<\/p><p>Here are a few <a href=\"https:\/\/datascientest.com\/en\/boosting-business-with-3-essential-machine-learning-algorithms\">examples of learning techniques:<\/a><\/p><ul><li><strong>Bagging:<\/strong> the aim is to reduce variance, in order to improve the stability of predictions. In this case, you need components with low bias and high variance. The biases then counteract the variances. This makes the results less sensitive to the specifics of the training data.<\/li><li><strong>Boosting:<\/strong> conversely, the aim is to reduce bias. To achieve this, models are run sequentially.<\/li><li><strong>Stacking:<\/strong> improves accuracy while keeping variance and bias low. This is particularly useful for random forests.<\/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-2fc2933 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"2fc2933\" 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\/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\">Learn to design models<\/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-bb5ec4a elementor-widget elementor-widget-text-editor\" data-id=\"bb5ec4a\" 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 principle,<strong> ensembling<\/strong> runs the models separately before combining the predictions. This model assembly then reduces the variance of predictions obtained on <a href=\"https:\/\/datascientest.com\/en\/data-architecture-definition-and-importance-in-data-science\">test and validation data.<\/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-e48830e elementor-widget elementor-widget-text-editor\" data-id=\"e48830e\" 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=\"text-decoration: underline;\"><strong>Good to know:<\/strong><\/span><\/p><p>Although the combination of several models can produce more reliable predictions, the number of models should be limited. This is both because of the costs involved in training, but also because of a drop in performance linked to the addition of new models.<\/p><p>Generally speaking, the right balance is between 3 and 10 trained models. This is the number that will deliver the best results.<\/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-01542eb elementor-widget elementor-widget-image\" data-id=\"01542eb\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/05\/ensembling-datascientest-1.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-56da835 e-flex e-con-boxed e-con\" data-id=\"56da835\" 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-55067ba elementor-widget elementor-widget-heading\" data-id=\"55067ba\" 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 advantages?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a854e55 elementor-widget elementor-widget-text-editor\" data-id=\"a854e55\" 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>Ensembling is one of the most commonly used <a href=\"https:\/\/datascientest.com\/en\/automl-and-machine-learning-automation-a-threat-to-data-scientists\">Machine Learning techniques<\/a>. And with good reason: it offers data experts a multitude of benefits:<\/p><ul><li><strong>Accuracy:<\/strong> in general, model assemblies offer better results than any single-component model. In most cases, results improve with the size of the ensemble. But as we saw earlier, it&#8217;s important to strike the right balance. In other words, don&#8217;t add too many models.<\/li><li><strong>Relevance<\/strong>: model assemblies are better able to generalize in response to a given problem. This limits the dispersion of analyses.<\/li><li><strong>Flexibility:<\/strong> with ensembling, Data Scientists have access to large quantities of data, but it is entirely possible to adapt results to the level of information available. In particular, they can select the best model from among several choices.<\/li><\/ul>\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-e3ba414 e-flex e-con-boxed e-con\" data-id=\"e3ba414\" 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-e4c43b6 elementor-widget elementor-widget-heading\" data-id=\"e4c43b6\" 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 limits?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a3627eb elementor-widget elementor-widget-text-editor\" data-id=\"a3627eb\" 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>Although ensembling is very effective, it does have certain limitations. For example:<\/p><ul><li>Only models trained with the same parameters can be combined;<\/li><li>Some learning models cannot be assembled. This is particularly true of models trained using K-Fold <a href=\"https:\/\/datascientest.com\/en\/the-importance-of-cross-validation\">cross-testing,<\/a> with vectors or image features, partitioned models or computer vision models.<\/li><\/ul>\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-42007b0 e-flex e-con-boxed e-con\" data-id=\"42007b0\" 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-13dd854 elementor-widget elementor-widget-heading\" data-id=\"13dd854\" 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\">Things to remember :<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9d439b7 elementor-widget elementor-widget-text-editor\" data-id=\"9d439b7\" 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\">Ensembling combines several neural network models to optimize the relevance of predictive results.<\/li><li style=\"font-weight: 400;\" aria-level=\"1\">Prior to assembly, each model is run separately, with its own learning techniques.<\/li><li style=\"font-weight: 400;\" aria-level=\"1\">Model ensemble learning improves the accuracy and relevance of predictions.<\/li><li style=\"font-weight: 400;\" aria-level=\"1\">That said, it is not possible to use this technique with all models.<\/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-5a5e6da elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"5a5e6da\" 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\/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\">Starting a Machine Learning 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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a6eca6f e-flex e-con-boxed e-con\" data-id=\"a6eca6f\" 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-05926fa elementor-widget elementor-widget-html\" data-id=\"05926fa\" 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\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is ensembling?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Ensembling combines several neural network models to solve a given problem. 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[&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":179837,"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>The Power of Ensemble Methods in Data Science<\/title>\n<meta name=\"description\" content=\"Discover the concept of ensembling in data science and how it leverages multiple models to improve predictive accuracy and robustness. 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