{"id":173322,"date":"2023-11-12T15:17:33","date_gmt":"2023-11-12T14:17:33","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=173322"},"modified":"2023-11-12T15:18:45","modified_gmt":"2023-11-12T14:18:45","slug":"cracking-the-code-on-underfitting-in-machine-learning","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/cracking-the-code-on-underfitting-in-machine-learning","title":{"rendered":"Cracking the Code on Underfitting in Machine Learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"173322\" class=\"elementor elementor-173322\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2c5c83a6 e-flex e-con-boxed e-con\" data-id=\"2c5c83a6\" 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-51db046a elementor-widget elementor-widget-heading\" data-id=\"51db046a\" 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\">Defining the right model is essential for making relevant predictions in Machine Learning. But poorly fitting training data can affect the performance of predictive analyses. This is precisely what happens with underfitting. So what is underfitting? And how can it be avoided? The answers are here.<\/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-11682b6f e-flex e-con-boxed e-con\" data-id=\"11682b6f\" 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-76f36d0e elementor-widget elementor-widget-heading\" data-id=\"76f36d0e\" 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 underfitting?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d924bfe elementor-widget elementor-widget-text-editor\" data-id=\"2d924bfe\" 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>Underfitting is a <a href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\">key concept in Machine Learning<\/a>, since it can be responsible for poor performance in supervised learning from data. So before looking at this concept in more detail, it&#8217;s worth recalling a few essential elements of how <a href=\"https:\/\/datascientest.com\/en\/scikit-learn-discover-the-python-library-dedicated-to-machine-learning\">Machine Learning works.<\/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-404f4b7a e-flex e-con-boxed e-con\" data-id=\"404f4b7a\" 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-2aad3f33 elementor-widget elementor-widget-heading\" data-id=\"2aad3f33\" 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\">A reminder of how Machine Learning works<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-33b6e224 elementor-widget elementor-widget-text-editor\" data-id=\"33b6e224\" 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 <a href=\"https:\/\/datascientest.com\/en\/k-means-clustering-in-machine-learning-a-deep-dive\">aim of Machine Learning<\/a> is to predict a pattern using learning models based on as yet unknown data.<\/p><p>To achieve this, supervised learning is based on two key ideas. These are<\/p><ul><li><strong>Approximation:<\/strong> this involves training the training data (or training set) to obtain a model with the lowest possible error rate.<\/li><li><strong>Generalisation:<\/strong> following this training, data scientists use validation data to test the different models. This enables them to design a model that can be generalised to new, never-before-seen data.<\/li><\/ul><p>\u00a0<\/p><p>The overall idea is to obtain similar levels of error in training and validation.<\/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-536630c0 elementor-widget elementor-widget-image\" data-id=\"536630c0\" 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=\"802\" height=\"539\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/machine-learning-underfitting.jpg\" class=\"attachment-full size-full wp-image-173326\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/machine-learning-underfitting.jpg 802w, https:\/\/datascientest.com\/en\/files\/2023\/11\/machine-learning-underfitting-300x202.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/11\/machine-learning-underfitting-768x516.jpg 768w\" sizes=\"(max-width: 802px) 100vw, 802px\" \/>\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-4a69d8a4 e-flex e-con-boxed e-con\" data-id=\"4a69d8a4\" 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-2a51b46f elementor-widget elementor-widget-heading\" data-id=\"2a51b46f\" 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\">Underfitting or underlearning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c49a050 elementor-widget elementor-widget-text-editor\" data-id=\"c49a050\" 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>Underfitting<\/strong> can be translated as underadjustment or underlearning. In this case, we also say that the model suffers from a large Bias.<\/p><p>This occurs when<strong> only relevant variables<\/strong> are named in relation to the problem, or when the model is forced to stop learning prematurely. As a result, the model created is too simplistic and risks missing the general trend.<\/p><p>In fact, an <strong>under-tuned model adapts poorly<\/strong> to the training set. It will make biased predictions due to a lack of sufficient training, and will reveal a large number of errors during the learning phase.<\/p><p>In other words, it is a learning model that is too generalist and fails to <strong>provide accurate predictive analyses.<\/strong><\/p><p>It is therefore vital to master this<a href=\"https:\/\/datascientest.com\/en\/unlock-your-future-dive-into-machine-learning-engineer-training\"> concept in Machine Learning<\/a> before designing appropriate models. But before we look at how to avoid underfitting, we&#8217;ll also look at its opposite.<\/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-a86de97 e-flex e-con-boxed e-con\" data-id=\"a86de97\" 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-21a74f06 elementor-widget elementor-widget-heading\" data-id=\"21a74f06\" 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\">Overfitting, the opposite of underfitting<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6c79b1aa elementor-widget elementor-widget-text-editor\" data-id=\"6c79b1aa\" 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 contrast to underfitting, <a href=\"https:\/\/datascientest.com\/en\/overfitting-what-is-it-how-can-i-avoid-it\">overfitting means over-adjusting or over-learning.<\/a><\/p><p>Here, we use as many variables as possible to be prepared for all hypotheses. But beware, there is a risk of using too many variables that are irrelevant to the study in hand.<\/p><p>While this method makes it<strong> possible to obtain similar levels of error in learning and validation<\/strong>, it makes the model much more complex. This implies an increase in the number of parameters to be estimated. As a result, the model will detect more spurious correlations.<\/p><p>The over-fitted model focuses on memorising the very specific characteristics of the data. It is said to adjust to the noise in the data.<\/p><p>Eventually, it will find causal links between unrelated data. It can then perform very well on training data, but to the detriment of the general trend.<\/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-2bf87320 elementor-widget elementor-widget-image\" data-id=\"2bf87320\" 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=\"1055\" height=\"629\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/underfitting-vs-overfitting-e1670513276723.png\" class=\"attachment-full size-full wp-image-173327\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/underfitting-vs-overfitting-e1670513276723.png 1055w, https:\/\/datascientest.com\/en\/files\/2023\/11\/underfitting-vs-overfitting-e1670513276723-300x179.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/11\/underfitting-vs-overfitting-e1670513276723-1024x611.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/11\/underfitting-vs-overfitting-e1670513276723-768x458.png 768w\" sizes=\"(max-width: 1055px) 100vw, 1055px\" \/>\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-3f0b8270 e-flex e-con-boxed e-con\" data-id=\"3f0b8270\" 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-57c6032c elementor-widget elementor-widget-heading\" data-id=\"57c6032c\" 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 can I avoid underfitting?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-60c8b9df elementor-widget elementor-widget-text-editor\" data-id=\"60c8b9df\" 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<a href=\"https:\/\/datascientest.com\/en\/4-blockchain-data-science-projects-to-discover\"> challenge for Data Scientists<\/a> is to design the right model, neither too simple nor too complex. In other words, avoiding underfitting, without falling into overfitting.<\/p><p>To do this, you need to identify the sweet spot that will enable you to strike the right balance. But first you need to learn how to <strong>detect underfitting and overfitting.<\/strong><\/p><p>If the test data has a low error rate, but the training data has a high error rate, there is probably underfitting.<\/p><p>In this case, be careful not to make the model too complex. Here, the opposite situation will occur: training data with a low error rate and test data with a high error rate. <strong>Overfitting is typical.<\/strong><\/p><p>Once you are able to identify one or the other, you can refine the predictive model during the learning phase. This gradually reduces the errors in the training set.<\/p><p>The data scientists must continue to refine the model until the errors increase in the validation phase. The equilibrium lies just before these errors increase at this stage.<\/p><p>By applying this method, you can detect the dominant trend and apply it widely to <a href=\"https:\/\/datascientest.com\/en\/datasets-top-5-places-to-find-quality-datasets\">new data sets.<\/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-6e0b93f5 e-flex e-con-boxed e-con\" data-id=\"6e0b93f5\" 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-2c790255 elementor-widget elementor-widget-heading\" data-id=\"2c790255\" 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\">DataScientest for mastering Machine Learning models<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9060088 elementor-widget elementor-widget-text-editor\" data-id=\"9060088\" 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>Finding the <strong>right balance between underfitting and overfitting<\/strong> requires practice and increased knowledge of data analysis.<\/p><p>That&#8217;s precisely why DataScientest offers a range of Machine Learning training courses. Take a look at our programmes.<\/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-7ea239e8 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"7ea239e8\" 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 the DataScientest Trainings<\/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-23e045ae e-flex e-con-boxed e-con\" data-id=\"23e045ae\" 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\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>Defining the right model is essential for making relevant predictions in Machine Learning. But poorly fitting training data can affect the performance of predictive analyses. This is precisely what happens with underfitting. So what is underfitting? And how can it be avoided? The answers are here. What is underfitting? Underfitting is a key concept in [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":173324,"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>Cracking the Code on Underfitting in Machine Learning<\/title>\n<meta name=\"description\" content=\"Uncover the mysteries of underfitting in machine learning. 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