{"id":167191,"date":"2023-03-22T10:59:05","date_gmt":"2023-03-22T09:59:05","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=167191"},"modified":"2023-10-30T18:47:12","modified_gmt":"2023-10-30T17:47:12","slug":"management-of-unbalanced-classification-problems-i","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-i","title":{"rendered":"Managing Unbalanced Classification Problems &#8211; Part 1"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"167191\" class=\"elementor elementor-167191\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cfc8834 e-flex e-con-boxed e-con\" data-id=\"cfc8834\" 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-603830e elementor-widget elementor-widget-text-editor\" data-id=\"603830e\" 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;\">Classification of unbalanced data is a <\/span><b>classification problem<\/b><span style=\"font-weight: 400;\"> where the training sample contains a strong disparity between the classes to be predicted. This problem is frequently encountered in <\/span><b>binary classification problems<\/b><span style=\"font-weight: 400;\">, especially in anomaly detection.<\/span><\/p><p><span style=\"font-weight: 400;\">This paper will be divided into <\/span><b>two parts<\/b><span style=\"font-weight: 400;\">: The first one focuses on the choice of metrics specific to this type of data, and the second one details the range of useful methods to obtain a successful model.\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-7c49818 e-flex e-con-boxed e-con\" data-id=\"7c49818\" 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-e2a43f3 elementor-widget elementor-widget-heading\" data-id=\"e2a43f3\" 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\">Part I: Choosing the right metrics<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c266111 elementor-widget elementor-widget-heading\" data-id=\"c266111\" 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\">What is an evaluation metric? \n<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4f52b75 elementor-widget elementor-widget-text-editor\" data-id=\"4f52b75\" 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;\">An evaluation metric quantifies the performance of a predictive model. Choosing the right metric is almost mandatory when evaluating <\/span><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\"><b>Machine Learning models<\/b><\/a><span style=\"font-weight: 400;\">, and the quality of a classification model depends directly on the metric used to evaluate it.<\/span><\/p><p><span style=\"font-weight: 400;\">About classification problems, metrics generally consist in<\/span><b> comparing the real classes<\/b><span style=\"font-weight: 400;\"> to the classes predicted by the model. They can also be used to interpret the predicted probabilities for these classes.<\/span><\/p><p><span style=\"font-weight: 400;\">One of the<\/span><b> key performance<\/b> <b>concepts<\/b><span style=\"font-weight: 400;\"> for classification is the confusion matrix, which is a tabular visualization of the model predictions against the true labels. Each row of the confusion matrix represents the instances of a real class and each column represents the instances of a predicted class.<\/span><\/p><p><span style=\"font-weight: 400;\">Let&#8217;s take the example of a <\/span><b>binary classification<\/b><span style=\"font-weight: 400;\">, where we have 100 positive instances and 70 negative instances.<\/span><\/p><p><span style=\"font-weight: 400;\">The confusion matrix below corresponds to the results obtained by our model:<\/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-258cac4 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"258cac4\" 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=\"800\" height=\"450\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/Predicted-classes-1-1024x576.jpg\" class=\"attachment-large size-large wp-image-167235\" alt=\"Predicted classes\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/Predicted-classes-1-1024x576.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/03\/Predicted-classes-1-300x169.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/03\/Predicted-classes-1-768x432.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/03\/Predicted-classes-1-1536x864.jpg 1536w, https:\/\/datascientest.com\/en\/files\/2023\/03\/Predicted-classes-1.jpg 1920w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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-1d1a6d6 elementor-widget elementor-widget-text-editor\" data-id=\"1d1a6d6\" 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;\">It gives an overview of the correct and false predictions.<\/span><\/p><p><span style=\"font-weight: 400;\">To summarize this matrix in a metric, it is possible to use the rate of good predictions or accuracy. Here it is equal to (90+57)\/170 = 0.86.<\/span><\/p><p><span style=\"font-weight: 400;\">The choice of an appropriate metric is <\/span><b>not obvious<\/b><span style=\"font-weight: 400;\"> for any Machine Learning model, but it is particularly difficult for unbalanced classification problems.<\/span><\/p><p><span style=\"font-weight: 400;\">In the case of data with a <\/span><b>strong majority class<\/b><span style=\"font-weight: 400;\">, classical algorithms are often biased because their loss functions try to optimize metrics such as the rate of good predictions, without taking in account the data allocation.<\/span><\/p><p><span style=\"font-weight: 400;\">In the worst case, <\/span><b>minority classes<\/b><span style=\"font-weight: 400;\"> are treated as outliers of the majority class and the learning algorithm simply generates a trivial classifier that classifies each example into the majority class. The model will appear to perform well but this will only reflect the overrepresentation of the majority class. This is called <\/span><b>paradoxical accuracy<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">In most cases, the minority class represents the <\/span><b>greatest interest<\/b><span style=\"font-weight: 400;\"> and that we would like to be able to identify, as in the example of fraud detection.<\/span><\/p><p><span style=\"font-weight: 400;\">The <\/span><b>imbalance level varies<\/b><span style=\"font-weight: 400;\"> but the use cases are recurrent: disease screening test , failure detection, search engine, spam filtering, marketing targeting&#8230;<\/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-9719020 elementor-widget elementor-widget-heading\" data-id=\"9719020\" 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\">Practical application: Churn Rate <\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ca65a2 elementor-widget elementor-widget-text-editor\" data-id=\"2ca65a2\" 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;\">Let&#8217;s assume that a service company wants to predict its<\/span><b> churn rate<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Little reminder: the <\/span><b>churn rate\u2019s ratio<\/b><span style=\"font-weight: 400;\"> is the following : lost customers \/ total number of customers, measured over a given period, usually one year.<\/span><\/p><p><span style=\"font-weight: 400;\">The company wants to predict for each customer whether he will end his contract at the end of the year.<\/span><\/p><p><span style=\"font-weight: 400;\">We have a dataset containing personal information and contract characteristics of each customer of the company for the year X, as well as a variable to know if he renewed his contract at the end of the year.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">In our data, the number of &#8216;churners&#8217; corresponds to 11% of the total number of customers.<\/span><\/p><p><span style=\"font-weight: 400;\">We decide to train a <\/span><b>first logistic regression model <\/b><span style=\"font-weight: 400;\">on our prepared and normalized data.<\/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-97fe537 elementor-widget elementor-widget-html\" data-id=\"97fe537\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script src=\"https:\/\/gist.github.com\/Raphkas15\/473a8d6ef5535da5e1c02cdfd1977030.js\"><\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d468baf elementor-widget elementor-widget-text-editor\" data-id=\"d468baf\" 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;\">Surprise! Our code displays a good prediction rate of 0.90!<\/span><\/p><p><span style=\"font-weight: 400;\">This is a very good score, but our goal is to<\/span><b> predict<\/b><span style=\"font-weight: 400;\"> the possible departure of customers. Does this result mean that out of 10 churners, 9 will be identified as such by the model? No!<\/span><\/p><p><span style=\"font-weight: 400;\">The only interpretation that can be made is that <\/span><b>9 out of 10 customers<\/b><span style=\"font-weight: 400;\"> have been well classified by the model.<\/span><\/p><p><span style=\"font-weight: 400;\">To successfully detect naive behavior in a model, the most effective tool is always the confusion matrix.<\/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-3510ff2 elementor-widget elementor-widget-html\" data-id=\"3510ff2\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script src=\"https:\/\/gist.github.com\/Raphkas15\/48118451ce0bfb203caf95c55dd6d8d8.js\"><\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f0f1668 elementor-widget elementor-widget-text-editor\" data-id=\"f0f1668\" 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 first look at the confusion matrix shows us that the good prediction rate obtained is largely influenced by the <\/span><b>good behavior of the model <\/b><span style=\"font-weight: 400;\">on the dominant class (0).<\/span><\/p><p><span style=\"font-weight: 400;\">In order to evaluate the model related to the desired behavior on a class, it is possible to use a series of metrics from the confusion matrix, such as precision, recall, and f1-score, defined below.<\/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-bd0d046 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"bd0d046\" 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=\"512\" height=\"89\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/image-3.png\" class=\"attachment-large size-large wp-image-167199\" alt=\"F1-score\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/image-3.png 512w, https:\/\/datascientest.com\/en\/files\/2023\/03\/image-3-300x52.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\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-d29b678 elementor-widget elementor-widget-text-editor\" data-id=\"d29b678\" 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>Thus for a given class:<\/p><ul><li aria-level=\"1\">A high precision and a high recall -&gt; The class has been\u00a0<b>well managed<\/b>\u00a0by the model<\/li><li aria-level=\"1\">High precision and low recall -&gt; The\u00a0<b>class is not well detected<\/b>\u00a0but when it is, the model is very reliable.\u00a0<\/li><li aria-level=\"1\">Low precision and high recall -&gt; The\u00a0<b>class is well detected<\/b>, but also includes observations of other classes.<\/li><li aria-level=\"1\">Low precision and low recall -&gt; the<b>\u00a0class has not been handled well\u00a0<\/b>at all<\/li><\/ul><p>The F1 score measures both precision and recall.<\/p><p>In the case of binary classification, the sensitivity and specificity correspond respectively to the recall of the positive and negative classes.<\/p><p>Another metric, the<b>\u00a0geometric mean (G-mean)<\/b>, is useful for unbalanced classification problems: it is the root of the product of sensitivity and specificity.<\/p><p>These different metrics are easily accessible thanks to the\u00a0<b>imblearn package<\/b>.\u00a0<\/p><p>The\u00a0<b>classification_report_imbalanced()<\/b>\u00a0function allows displaying a report containing the results on all the metrics of the package.<\/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-d5ce0ec elementor-widget elementor-widget-html\" data-id=\"d5ce0ec\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script src=\"https:\/\/gist.github.com\/Raphkas15\/a8f007d33300970f9ea62f19eeaa63bf.js\"><\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-92a5aa5 elementor-widget elementor-widget-text-editor\" data-id=\"92a5aa5\" 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;\">We obtain the following table:\u00a0<\/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-0938052 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"0938052\" 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=\"512\" height=\"288\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/image-1.png\" class=\"attachment-large size-large wp-image-167198\" alt=\"Confusion matrix\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/image-1.png 512w, https:\/\/datascientest.com\/en\/files\/2023\/03\/image-1-300x169.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\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-0d00528 elementor-widget elementor-widget-text-editor\" data-id=\"0d00528\" 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 table shows that the recall and f1-score for class 1 are bad, while for class 0 they are high. In addition, the geometric mean is also low.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Thus, the trained model <\/span><b>does not<\/b><span style=\"font-weight: 400;\"> fit our data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In&nbsp;<\/span><a href=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-ii\" target=\"_blank\"><b>Part II<\/b><\/a><span style=\"font-weight: 400;\">, we will discover methods that will allow us to obtain much better results.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You want to improve your skills to build efficient and reliable models from unbalanced data sets?<\/span><strong><a href=\"https:\/\/datascientest.com\/en\/data-courses\"> Discover all our learning modules<\/a><\/strong><span style=\"font-weight: 400;\">!&nbsp;<\/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-81bc2d3 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"81bc2d3\" 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\/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\">Start a Machine Learning Engineer 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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-67637c6 e-flex e-con-boxed e-con\" data-id=\"67637c6\" 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-127edc5 elementor-widget elementor-widget-html\" data-id=\"127edc5\" 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\": \"What is an evaluation metric?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"An evaluation metric quantifies the performance of a predictive model. Choosing the right metric is almost mandatory when evaluating Machine Learning models, and the quality of a classification model depends directly on the metric used to evaluate it.\"\r\n    }\r\n  }\r\n}\r\n<\/script>\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>Classification of unbalanced data is a classification problem where the training sample contains a strong disparity between the classes to be predicted. This problem is frequently encountered in binary classification problems, especially in anomaly detection. This paper will be divided into two parts: The first one focuses on the choice of metrics specific to this [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":167193,"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>Managing Unbalanced Classification Problems - Part 1<\/title>\n<meta name=\"description\" content=\"Classification of unbalanced data is a classification problem where the training sample contains a strong disparity between the classes to be predicted.\" \/>\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\/management-of-unbalanced-classification-problems-i\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Managing Unbalanced Classification Problems - Part 1\" \/>\n<meta property=\"og:description\" content=\"Classification of unbalanced data is a classification problem where the training sample contains a strong disparity between the classes to be predicted.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-i\" \/>\n<meta property=\"og:site_name\" content=\"Data Science Courses | DataScientest\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientestEN\" \/>\n<meta property=\"article:published_time\" content=\"2023-03-22T09:59:05+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-10-30T17:47:12+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/desequilibre_cost_sensitive_learning.png\" \/>\n\t<meta property=\"og:image:width\" content=\"614\" \/>\n\t<meta property=\"og:image:height\" content=\"466\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:site\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-i#article\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-i\"},\"author\":{\"name\":\"\",\"@id\":\"\"},\"headline\":\"Managing Unbalanced Classification Problems &#8211; 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