{"id":199425,"date":"2025-10-21T06:04:00","date_gmt":"2025-10-21T05:04:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=199425"},"modified":"2025-11-03T18:04:44","modified_gmt":"2025-11-03T17:04:44","slug":"all-about-one-hot-encoding","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-one-hot-encoding","title":{"rendered":"One-Hot Encoding: What is it? How to use it?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"199425\" class=\"elementor elementor-199425\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-96125a1 e-flex e-con-boxed e-con\" data-id=\"96125a1\" 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-0dd180e elementor-widget elementor-widget-text-editor\" data-id=\"0dd180e\" 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>One-Hot Encoding is a technique that allows categorical data to be converted into numerical vectors that can be utilized by machine learning models. Discover everything you need to know about this method!<\/b><\/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-40611fb elementor-widget elementor-widget-text-editor\" data-id=\"40611fb\" 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\/digitising-variables-for-your-machine-learning-algorithms\">Machine learning algorithms<\/a> are incredible computational tools, but they have a major limitation: they do not comprehend text. For these systems, words like \u201cblue,\u201d \u201cParis,\u201d or \u201ccat\u201d are merely noise. However, in real-world <\/span><b>datasets<\/b><span style=\"font-weight: 400;\">, <\/span><b>categorical variables<\/b><span style=\"font-weight: 400;\"> are prevalent. Product names, marital status, country, type of credit card\u2026 These non-numeric columns can make up to 40% of the data processed in <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-artificial-intelligence-and-finance-sector\"><span style=\"font-weight: 400;\">AI use cases<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">To make them interpretable by our models, they must be translated. One of the simplest and most popular methods for this is <\/span><b>One-Hot Encoding<\/b><span style=\"font-weight: 400;\">: a technique that transforms each category into a readable, unambiguous <\/span><b>binary vector<\/b><span style=\"font-weight: 400;\">. Despite its simplicity, it is a double-edged sword: useful, yet sometimes cumbersome. Let&#8217;s explore why, when, and how to use it effectively!<\/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-b037f64 e-flex e-con-boxed e-con\" data-id=\"b037f64\" 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-7ba2503 elementor-widget elementor-widget-heading\" data-id=\"7ba2503\" 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-large\">Categorical variables: a ubiquitous challenge\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b04696f elementor-widget elementor-widget-text-editor\" data-id=\"b04696f\" 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;\">They appear in every <\/span><b>data table<\/b><span style=\"font-weight: 400;\">, although they often go unnoticed at first glance: <\/span><b>categorical variables<\/b><span style=\"font-weight: 400;\"> are the columns that don\u2019t consist of numbers, but rather <\/span><b><i>names<\/i><\/b><span style=\"font-weight: 400;\">, <\/span><b><i>types<\/i><\/b><span style=\"font-weight: 400;\">, <\/span><b><i>statuses<\/i><\/b><span style=\"font-weight: 400;\">. For instance, if you have a \u201ccolor\u201d column with values \u201cred,\u201d \u201cgreen,\u201d \u201cblue,\u201d you\u2019re dealing with a categorical variable.<\/span><\/p><p><span style=\"font-weight: 400;\">The problem? Machine learning algorithms, whether they are linear, tree-based, or <a href=\"https:\/\/datascientest.com\/en\/all-about-artificial-neural-networks\">neural networks<\/a>, can only handle numbers. If you leave <\/span><b>strings in your data<\/b><span style=\"font-weight: 400;\">, you can be assured that your models will fail, or worse, learn incorrectly.<\/span><\/p><p><span style=\"font-weight: 400;\">Simply <\/span><b>converting these strings into arbitrary numbers<\/b><span style=\"font-weight: 400;\"> (\u201cred\u201d = 1, \u201cgreen\u201d = 2, etc.) is not enough. In such a case, the model interprets these numbers as having a hierarchy or distance, which is often incorrect. This is why <\/span><b>encoding<\/b><span style=\"font-weight: 400;\"> is a vital step in preprocessing! <\/span><b>One-Hot Encoding<\/b><span style=\"font-weight: 400;\"> is often the default solution. But before diving in, let\u2019s thoroughly understand its principle.<\/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-e5ddb45 elementor-widget elementor-widget-image\" data-id=\"e5ddb45\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-1.webp\" class=\"attachment-full size-full wp-image-199428\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-1.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-1-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-1-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-1-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-d16aab0 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d16aab0\" 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\">Deepen your understanding of One-Hot Encoding<\/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-757059f e-flex e-con-boxed e-con\" data-id=\"757059f\" 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-96395d7 elementor-widget elementor-widget-heading\" data-id=\"96395d7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">The principle that transforms text into vectors<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7e29793 elementor-widget elementor-widget-text-editor\" data-id=\"7e29793\" 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;\">One-Hot Encoding, which might be <\/span><b>translated as \u201cbinary encoding\u201d<\/b><span style=\"font-weight: 400;\">, is based on a simple yet remarkably effective idea: create a column for each possible value of a variable, then activate only the one that corresponds to the observed data, filling the others with zeros.<\/span><\/p><p><span style=\"font-weight: 400;\">Consider an example: a \u201ccolor\u201d column with values \u201cred,\u201d \u201cgreen,\u201d and \u201cblue.\u201d In One-Hot Encoding, you create three columns: <\/span><i><span style=\"font-weight: 400;\">color_red<\/span><\/i><span style=\"font-weight: 400;\">, <\/span><i><span style=\"font-weight: 400;\">color_green<\/span><\/i><span style=\"font-weight: 400;\">, and <\/span><i><span style=\"font-weight: 400;\">color_blue<\/span><\/i><span style=\"font-weight: 400;\">. If a row contains \u201cred,\u201d you put a 1 in <\/span><i><span style=\"font-weight: 400;\">color_red<\/span><\/i><span style=\"font-weight: 400;\">, and 0s in the other two. If it\u2019s \u201cblue,\u201d it will be 1 in <\/span><i><span style=\"font-weight: 400;\">color_blue<\/span><\/i><span style=\"font-weight: 400;\">, and 0 elsewhere. The benefit? <\/span><b>It prevents a fictional order among the categories<\/b><span style=\"font-weight: 400;\">. Encoding \u201cred\u201d = 1, \u201cgreen\u201d = 2, \u201cblue\u201d = 3 might lead an algorithm to think \u201cblue\u201d &gt; \u201cgreen\u201d &gt; \u201cred\u201d\u2026 although these values have no ordinal meaning.<\/span><\/p><p><span style=\"font-weight: 400;\">Through <\/span><b>One-Hot Encoding<\/b><span style=\"font-weight: 400;\">, each category is treated as a standalone entity, without any numerical or hierarchical association with the others. This prevents models from introducing <\/span><b>bias related to a false data structure<\/b><span style=\"font-weight: 400;\">. This type of encoding is therefore perfectly suited to <\/span><b>nominal categorical variables<\/b><span style=\"font-weight: 400;\">, meaning without any order logic (such as gender, city, or contract type).<\/span><\/p><p><span style=\"font-weight: 400;\">Yet, this approach also has its drawbacks, especially when the number of categories escalates.<\/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-89e5fd7 e-flex e-con-boxed e-con\" data-id=\"89e5fd7\" 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-da953c3 elementor-widget elementor-widget-heading\" data-id=\"da953c3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Implementation in Python<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-684ec07 elementor-widget elementor-widget-text-editor\" data-id=\"684ec07\" 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;\">One-Hot Encoding is neat, devoid of hierarchy bias, and compatible with all models. However, it has a significant structural flaw: it creates <\/span><b>a multitude of columns<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">For a practical example, consider working with an ecommerce dataset where a \u201cproduct\u201d variable contains <\/span><b>1,200 different references<\/b><span style=\"font-weight: 400;\">. Post-One-Hot Encoding, you will have <\/span><b>1,200 columns<\/b><span style=\"font-weight: 400;\">. This is more than an aesthetic issue. It becomes a time bomb for your model. Two specific issues arise.<\/span><\/p><p><span style=\"font-weight: 400;\">First, <\/span><b>a dimensionality explosion<\/b><span style=\"font-weight: 400;\">, as each new category creates a column, considerably burdening the model, extending training time, and potentially leading to <\/span><a href=\"https:\/\/datascientest.com\/en\/overfitting-what-is-it-how-can-i-avoid-it\"><span style=\"font-weight: 400;\">overfitting<\/span><\/a><span style=\"font-weight: 400;\">, particularly with sensitive models like <\/span><a href=\"https:\/\/datascientest.com\/en\/knn-what-is-the-knn-algorithm\"><span style=\"font-weight: 400;\">KNNs<\/span><\/a><span style=\"font-weight: 400;\"> or <strong>decision trees<\/strong>.<\/span><\/p><p><span style=\"font-weight: 400;\">Second issue: <\/span><b>extreme sparsity<\/b><span style=\"font-weight: 400;\">. In a matrix of 1,200 columns, there is only one 1 per row, leading to over <\/span><b>95% zeros<\/b><span style=\"font-weight: 400;\"> in most cases. Storing this in a dense format poses a challenge for your RAM!<\/span><\/p><p><span style=\"font-weight: 400;\">Therefore, One-Hot Encoding is not scalable without caution. When dealing with <\/span><b>high cardinality variables<\/b><span style=\"font-weight: 400;\">, it&#8217;s crucial to think twice before opting for \u201cencode.\u201d<\/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-f188248 elementor-widget elementor-widget-image\" data-id=\"f188248\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-2.webp\" class=\"attachment-full size-full wp-image-199429\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-2.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-2-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-2-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-2-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-b8598e4 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"b8598e4\" 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\">Learn how to make data readable for AI<\/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-7c3d99d e-flex e-con-boxed e-con\" data-id=\"7c3d99d\" 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-65eb026 elementor-widget elementor-widget-heading\" data-id=\"65eb026\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">How far can we go? Critical thresholds to know<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b268cb5 elementor-widget elementor-widget-text-editor\" data-id=\"b268cb5\" 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;\">At what point should One-Hot Encoding be avoided due to a high number of modalities? No absolute rule exists, but community feedback suggests: <\/span><b>for more than 10 to 15 unique categories, consider alternatives<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Several problems accrue. <\/span><b>Multicollinearity<\/b><span style=\"font-weight: 400;\">, with the notorious <\/span><b>\u201cdummy trap\u201d<\/b><span style=\"font-weight: 400;\">. Creating a column per modality introduces total <\/span><b>redundancy<\/b><span style=\"font-weight: 400;\"> (the sum of all columns always gives 1). Some models may suffer from this, especially <a href=\"https:\/\/datascientest.com\/en\/linear-regression-with-python-how-does-it-work\">linear regressions<\/a>. Solution: remove a reference column.<\/span><\/p><p><b>Instability with small datasets<\/b><span style=\"font-weight: 400;\">. With limited observations but numerous categories, <\/span><b>the risk of overfitting is maximal<\/b><span style=\"font-weight: 400;\">. Plus, data can be unbalanced. Some categories might appear just once or twice, creating almost-empty columns that are unusable.<\/span><\/p><p><span style=\"font-weight: 400;\">In essence, the more modalities, the more One-Hot becomes a double-edged sword. Fortunately, it&#8217;s not the only tool in <a href=\"https:\/\/datascientest.com\/en\/essential-data-scientist-tools-for-your-daily-workflow\">a data scientist&#8217;s arsenal<\/a>.<\/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-68d2b00 e-flex e-con-boxed e-con\" data-id=\"68d2b00\" 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-3348611 elementor-widget elementor-widget-heading\" data-id=\"3348611\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">When to use One-Hot Encoding\u2026 or not<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e42ab57 elementor-widget elementor-widget-text-editor\" data-id=\"e42ab57\" 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;\">Selecting the right encoding method is critical. Here are cases where <\/span><b>One-Hot Encoding<\/b><span style=\"font-weight: 400;\"> is appropriate\u2026 and others where it should be avoided.<\/span><\/p><p><span style=\"font-weight: 400;\">This approach suits <\/span><b>variables with few unique modalities<\/b><span style=\"font-weight: 400;\"> (country, gender, type of contract\u2026), or with <\/span><b>balanced data<\/b><span style=\"font-weight: 400;\">, without rare categories.<\/span><\/p><p><span style=\"font-weight: 400;\">It&#8217;s also ideal for <\/span><b>simple or linear models<\/b><span style=\"font-weight: 400;\"> (<strong>logistic regression<\/strong>, <\/span><a href=\"https:\/\/datascientest.com\/en\/svm-support-vector-machines-unveiled-understanding-implementation-significance\"><span style=\"font-weight: 400;\">SVM<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/datascientest.com\/en\/perceptron-definition-and-use-cases\"><span style=\"font-weight: 400;\">perceptron<\/span><\/a><span style=\"font-weight: 400;\">), or for explainable use cases: churn, customer scoring, marketing.<\/span><\/p><p><span style=\"font-weight: 400;\">Conversely, it should be avoided for columns with numerous categories. Ditto for sparsely populated datasets or those with many missing values.<\/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-303d65c elementor-widget elementor-widget-image\" data-id=\"303d65c\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-3.webp\" class=\"attachment-full size-full wp-image-199430\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-3.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-3-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-3-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-3-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-d0b3280 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d0b3280\" 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\">Master One-Hot Encoding<\/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-7db3258 e-flex e-con-boxed e-con\" data-id=\"7db3258\" 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-26d7626 elementor-widget elementor-widget-heading\" data-id=\"26d7626\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Smart alternatives<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cba98f2 elementor-widget elementor-widget-text-editor\" data-id=\"cba98f2\" 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 category numbers balloon, <\/span><b>One-Hot Encoding<\/b><span style=\"font-weight: 400;\"> reveals its limitations. Fortunately, <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-machine-learning-metrics\"><span style=\"font-weight: 400;\">the machine learning ecosystem<\/span><\/a><span style=\"font-weight: 400;\"> provides several alternatives, each suited for specific use cases.<\/span><\/p><p><span style=\"font-weight: 400;\">With <\/span><b>Label Encoding<\/b><span style=\"font-weight: 400;\">, each category is replaced by a unique integer. <\/span><b>Simple and fast<\/b><span style=\"font-weight: 400;\">, but best avoided with linear or distance-based models, as it introduces <\/span><b>an artificial order<\/b><span style=\"font-weight: 400;\"> (\u201cgreen\u201d = 1, \u201cred\u201d = 2, \u201cblue\u201d = 3\u2026). Yet, it&#8217;s effective for decision trees, which are order-insensitive.<\/span><\/p><p><span style=\"font-weight: 400;\">Another strategy is <\/span><b>Target Encoding<\/b><span style=\"font-weight: 400;\">, also known as <\/span><b>Mean Encoding<\/b><span style=\"font-weight: 400;\">. Each category is replaced by that category&#8217;s average target variable. For instance: if \u201cPremium\u201d customers purchase at an average of 300 \u20ac, encode \u201cPremium\u201d with 300. This is powerful for high-cardinality variables. However, beware of data leakage risk if employed without proper <\/span><a href=\"https:\/\/datascientest.com\/en\/the-importance-of-cross-validation\"><span style=\"font-weight: 400;\">cross-validation<\/span><\/a><span style=\"font-weight: 400;\">. Also, be cautious of overfitting.<\/span><\/p><p><span style=\"font-weight: 400;\">Moreover, popularized by <\/span><b>deep learning<\/b><span style=\"font-weight: 400;\">, \u201cembeddings\u201d convert categories into model-learned <\/span><b>continuous vectors<\/b><span style=\"font-weight: 400;\">. Each modality becomes a point in a <\/span><b>vector space<\/b><span style=\"font-weight: 400;\">, near others with similar behaviors. It&#8217;s ultra-effective on vast datasets, particularly in <\/span><a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\"><span style=\"font-weight: 400;\">NLP<\/span><\/a><span style=\"font-weight: 400;\"> or product recommendations.<\/span><\/p><p><span style=\"font-weight: 400;\">Lastly, there is <\/span><b>Feature Hashing<\/b><span style=\"font-weight: 400;\">. Instead of explicitly encoding each category, it uses a hash function to assign categories to columns within a fixed number. Less readable but effective in avoiding column explosion. Handle with caution, as it might cause several categories to accumulate in the same \u201cbucket,\u201d leading to a <\/span><b>\u201c<\/b><b><i>hash collision<\/i><\/b><b>\u201d<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">With these various strategies, choose the most fitting one according to dataset size, model used, and category nature.<\/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-48d5e10 e-flex e-con-boxed e-con\" data-id=\"48d5e10\" 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-158cc2b elementor-widget elementor-widget-heading\" data-id=\"158cc2b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">One-Hot Encoding and Deep Learning<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-502d9c1 elementor-widget elementor-widget-text-editor\" data-id=\"502d9c1\" 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 deep learning frameworks, one might think One-Hot Encoding is obsolete. Still, it remains extensively used\u2026 in certain cases. It&#8217;s <\/span><b>valuable in simple classification tasks with few classes<\/b><span style=\"font-weight: 400;\">, or when preprocessing short sequences (e.g.: characters or <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-multi-token-prediction\"><span style=\"font-weight: 400;\">tokens in NLP<\/span><\/a><span style=\"font-weight: 400;\">). It\u2019s also relevant where there\u2019s no <\/span><b>learnable embedding<\/b><span style=\"font-weight: 400;\"> or limited data.<\/span><\/p><p><span style=\"font-weight: 400;\">However, as soon as deeper issues are encountered (NLP, recommendation, user data\u2026), embeddings are preferred, being more compact, meaningful, and model-learned. A typical instance: instead of converting each word into an expansive binary column, we use a 300-dimension <\/span><b>vector embedding<\/b><span style=\"font-weight: 400;\"> that captures meaning, context, and similarities.<\/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-a3248dc elementor-widget elementor-widget-image\" data-id=\"a3248dc\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-5.webp\" class=\"attachment-full size-full wp-image-199431\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-5.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-5-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-5-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-5-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-30c5c78 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"30c5c78\" 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\">Learn to use AI for data processing<\/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-2fafef8 e-flex e-con-boxed e-con\" data-id=\"2fafef8\" 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-0a4407f elementor-widget elementor-widget-heading\" data-id=\"0a4407f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">One-Hot Encoding, a straightforward method to encode your categorical variables<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a882d51 elementor-widget elementor-widget-text-editor\" data-id=\"a882d51\" 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>One-Hot Encoding<\/b><span style=\"font-weight: 400;\"> can be considered the Swiss army knife for novice data scientists: easy to understand, compatible with nearly all models, and robust across a broad range of practical cases. However, pitfalls lurk beneath this simplicity: <\/span><b>proliferating columns, sparse matrices, overfitting, multicollinearity\u2026<\/b><span style=\"font-weight: 400;\"> all reasons not to use it naively. Well-guided, well-calibrated, it remains an essential preprocessing standard. Misapplied, it can be a drag on performance.<\/span><\/p><p><span style=\"font-weight: 400;\">To deepen your <\/span><span style=\"font-weight: 400;\">understanding of data preprocessing techniques<\/span><span style=\"font-weight: 400;\">, <\/span><b>encoding<\/b><span style=\"font-weight: 400;\">, machine learning, and deep learning, look no further than the Artificial Intelligence training courses from DataScientest. Our comprehensive programs will empower you to understand AI model foundations, manipulate real <\/span><b>datasets<\/b><span style=\"font-weight: 400;\">, and implement complete <\/span><b>pipelines<\/b><span style=\"font-weight: 400;\"> with Scikit-learn and <\/span><span style=\"font-weight: 400;\">TensorFlow<\/span><span style=\"font-weight: 400;\">. You will also learn to utilize the best of each method like <\/span><b>One-Hot<\/b><span style=\"font-weight: 400;\">, <\/span><b>embeddings<\/b><span style=\"font-weight: 400;\">, or <\/span><b>PCA<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Through a <\/span><b>practice-oriented approach and concrete cases<\/b><span style=\"font-weight: 400;\">, you will be equipped to deploy high-performance models and obtain a recognized certification. <\/span><a href=\"https:\/\/datascientest.com\/en\/data-courses\"><span style=\"font-weight: 400;\">Our courses<\/span><\/a><span style=\"font-weight: 400;\"> adapt to your pace: whether intensive BootCamp, continuous format, or alternation, with funding options like CPF or France Travail. Join <\/span><b>DataScientest<\/b><span style=\"font-weight: 400;\"> and make sense of the 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-78f4c7b elementor-widget elementor-widget-image\" data-id=\"78f4c7b\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-4.webp\" class=\"attachment-full size-full wp-image-199432\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-4.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-4-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-4-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/one-hot-encoding-datascientest-4-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-5e22eb1 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"5e22eb1\" 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\">Browse our AI training programs<\/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-37a2422 elementor-widget elementor-widget-text-editor\" data-id=\"37a2422\" 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;\">You now know everything about One-Hot Encoding. For further information on this topic, see our comprehensive article on Category Encoders and <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\"><span style=\"font-weight: 400;\">our article on Deep Learning<\/span><\/a><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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5917abe e-flex e-con-boxed e-con\" data-id=\"5917abe\" 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-3a1c710 elementor-widget elementor-widget-html\" data-id=\"3a1c710\" 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\": \"Categorical variables: a ubiquitous challenge\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Categorical variables are columns that don\u2019t consist of numbers, but rather names, types, or statuses, which must be translated for machine learning models.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"The principle that transforms text into vectors\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"One-Hot Encoding creates a column for each category value and activates only the one corresponding to the data, preventing false hierarchies.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Implementation in Python: pandas or scikit-learn, the showdown of encoders\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"One-Hot Encoding can create a multitude of columns, causing dimensionality issues and sparsity that burden model training and memory.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"How far can we go? Critical thresholds to know\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Beyond 10 to 15 unique categories, One-Hot Encoding may cause multicollinearity and overfitting, especially in small datasets.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"When to use One-Hot Encoding\u2026 or not\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Use it for variables with few categories or balanced data; avoid it for high-cardinality columns or datasets with many missing values.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Smart alternatives: when One-Hot is no longer enough\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Alternatives include Label Encoding, Target Encoding, embeddings, and Feature Hashing, each suited to specific models and data sizes.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"One-Hot Encoding and Deep Learning: alliance or obsolescence?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"One-Hot Encoding remains useful in simple tasks, but embeddings are preferred for complex or large-scale deep learning scenarios.\"\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>One-Hot Encoding is a technique that allows categorical data to be converted into numerical vectors that can be utilized by machine learning models. Discover everything you need to know about this method! Machine learning algorithms are incredible computational tools, but they have a major limitation: they do not comprehend text. For these systems, words like [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":199426,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427,2421],"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>One-Hot Encoding: What is it? How to use it?<\/title>\n<meta name=\"description\" content=\"One-Hot Encoding converts your categorical variables into vectors that can be used by AI. 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