{"id":183468,"date":"2024-04-15T05:11:00","date_gmt":"2024-04-15T04:11:00","guid":{"rendered":"https:\/\/liora.io\/en\/?p=183468"},"modified":"2026-02-06T08:10:20","modified_gmt":"2026-02-06T07:10:20","slug":"featuretools-what-are-they-what-is-it-used-for-in-machine-learning","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/featuretools-what-are-they-what-is-it-used-for-in-machine-learning","title":{"rendered":"Featuretools: What are they? What is it used for in Machine Learning?"},"content":{"rendered":"<style>\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><p><strong>Featuretools is an open source Python library created by Alteryx to automate feature engineering in Machine Learning. Find out everything you need to know about it: how it works, its benefits, use cases, etc.<\/strong><\/p>\t\t\n\t\t<p><a href=\"https:\/\/liora.io\/en\/sarsa-how-does-machine-learning-work\">In the field of Machine Learning<\/a>, the engineering of functionalities or features is an essential practice. The quality and relevance of the features extracted from the data can make all the difference between a relevant model and an ineffective one.<\/p><p>However, this process is often very laborious. It requires in-depth expertise and considerable effort.<\/p><p>To remedy this problem, the company <strong>Alteryx<\/strong> has created an innovative open source solution to automate <strong>Feature Engineering.<\/strong><\/p><p>Launched at the end of 2017, this tool automatically discovers complex relationships in data, generates meaningful features and frees data scientists from the most tedious tasks. Its name? Featuretools.<\/p><p>In this dossier, you&#8217;ll find out everything you need to know about this <a href=\"https:\/\/liora.io\/en\/pycaret-everything-you-need-to-know-about-this-python-library\">Python library<\/a> that is revolutionising the field of <strong>Machine Learning.<\/strong> First of all, let&#8217;s look at the importance of <strong>feature engineering.<\/strong><\/p>\t\t\n\t\t\t<h3>Functionality engineering: a pillar of success for Machine Learning<\/h3>\t\t\n\t\t<p><strong>Feature Engineering<\/strong> can be seen as the art of transforming raw data into usable information, and forms the basis of <a href=\"https:\/\/liora.io\/en\/gensim-the-python-library-for-topic-modelling\">modelling in Machine Learning.<\/a><\/p><p>This is because the ability of Machine Learning algorithms to extract meaningful patterns when capturing relationships between data depends on the quality of the features presented to them.<\/p><p>These features, or variables, act as discriminating characteristics to guide the model in its quest for understanding or prediction.<\/p><p>If they are relevant and informative, they enable the model to generalise from the training data while avoiding over-fitting. On the other hand, poorly designed or redundant functionalities can introduce noise and <strong>mislead the model.<\/strong><\/p><p>And this engineering task is not limited to the selection of relevant variables, but also involves the creation of new derived features that are often crucial for expressing complex relationships.<\/p><p>However, as <a href=\"https:\/\/liora.io\/en\/datasets-top-5-places-to-find-quality-datasets\">datasets<\/a> become larger and more complex, the manual creation of functionality quickly becomes a Herculean task requiring hours of painstaking manipulation. As a result, the potential of models is diminished.<\/p><p>It is in this context that the <strong>Featuretools<\/strong> tool created by Alteryx emerges as a providential solution, promising to automate the process and free up Machine Learning experts to focus on more strategic tasks.<\/p>\t\t\n\t\t\t<style>\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>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2023\/12\/Featuretools-1.jpg\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t<figcaption><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Training in Machine Learning<\/a><\/div><\/div>\n\n\t\t\t<h3>What is Featuretools?<\/h3>\t\t\n\t\t<p>At the <strong>crossroads of machine learning and data science,<\/strong> Featuretools is an <a href=\"https:\/\/liora.io\/en\/spacy-nlps-open-source-python-library\">open-source Python library<\/a> offering advanced tools for automating feature engineering.<\/p><p>Its main objective is to enable the automatic capture of complex relationships between data, eliminating the need to manually specify each feature.<\/p><p>This approach is particularly beneficial in situations where the relationships between entities are non-trivial or difficult to capture manually.<\/p><p>Using Featuretools speeds up the feature engineering process by automating the creation of new variables, but also enables the creation of complex features that are difficult to identify manually.<\/p><p>By exploiting relationships between entities, it can generate information-rich features that potentially improve the model&#8217;s ability to generalise to data not seen during training.<\/p><p>&nbsp;<\/p><p>?Related articles:<\/p><table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" data-sheets-root=\"1\"><colgroup><col width=\"656\"><\/colgroup><tbody><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Epoch : An essential notion in real-time programming&quot;}\" data-sheets-hyperlink=\"https:\/\/liora.io\/en\/epoch-an-essential-notion\"><a href=\"https:\/\/liora.io\/en\/epoch-an-essential-notion\" target=\"_blank\" rel=\"noopener\">Epoch : An essential notion in real-time programming<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Manifold Learning: What is this machine learning method?&quot;}\" data-sheets-hyperlink=\"https:\/\/liora.io\/en\/manifold-learning-what-is-this-machine-learning-method\"><a href=\"https:\/\/liora.io\/en\/manifold-learning-what-is-this-machine-learning-method\" target=\"_blank\" rel=\"noopener\">Manifold Learning: What is this machine learning method?<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kernel: everything you need to know about the Machine Learning method&quot;}\" data-sheets-hyperlink=\"https:\/\/liora.io\/en\/kernel-everything-you-need-to-know-about-the-machine-learning-method\"><a href=\"https:\/\/liora.io\/en\/kernel-everything-you-need-to-know-about-the-machine-learning-method\" target=\"_blank\" rel=\"noopener\">Kernel: everything you need to know about the Machine Learning method<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Bagging Machine Learning: What is it about?&quot;}\" data-sheets-hyperlink=\"https:\/\/liora.io\/en\/bagging-machine-learning-what-is-it-about\"><a href=\"https:\/\/liora.io\/en\/bagging-machine-learning-what-is-it-about\" target=\"_blank\" rel=\"noopener\">Bagging Machine Learning: What is it about?<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Machine Learning Engineer Bootcamp: Why is it interesting?&quot;}\" data-sheets-hyperlink=\"https:\/\/liora.io\/en\/machine-learning-engineer-bootcamp-why-is-it-interesting\"><a href=\"https:\/\/liora.io\/en\/machine-learning-engineer-bootcamp-why-is-it-interesting\" target=\"_blank\" rel=\"noopener\">Machine Learning Engineer Bootcamp: Why is it interesting?<\/a><\/td><\/tr><\/tbody><\/table>\t\t\n\t\t\t<h3>How does it work? What are the key components?<\/h3>\t\t\n\t\t<p><strong>Featuretools<\/strong> is based on several fundamental concepts. EntitySets provide a way to manage entity-relationship data in a structured way.<\/p><p>Each <strong>EntitySet<\/strong> is a<a href=\"https:\/\/liora.io\/en\/exadata-what-is-this-oracle-dbms-solution\"> data structure<\/a> that contains a set of entities (tables) and the relationships between these entities. This makes it possible to model complex data where several entities are linked to each other.<\/p><p>For example, in a<strong> product defect prediction scenario<\/strong>, an EntitySet could contain entities such as &#8220;Orders&#8221;, &#8220;Products&#8221; and &#8220;Customers&#8221;.<\/p><p>The relationships between these entities, such as orders linked to products and customers linked to orders, are defined in a way that allows Featuretools to understand the underlying structure of the data.<\/p><p>At the heart of Featuretools&#8217; automation process is Deep Feature Synthesis (DFS): a mechanism for automatically creating features by combining information from multiple entities.<\/p><p>This process explores the relationships defined in the EntitySet to create more complex features. Rather than just simple features, DFS helps to capture deeper patterns.<\/p><p>For example, suppose an EntitySet contains &#8216;Customers&#8217; and &#8216;Transactions&#8217; entities. <strong>DFS could automatically create a Featuretools representing the sum of the transaction amounts for each customer.<\/strong><\/p><p>Without any manual intervention, Featuretools is therefore able to generate meaningful features that capture the relationships between entities.<\/p><p>Primitives are the basic operations that can be applied when creating new features. There are two different categories.<\/p><p>Aggregation primitives include operations such as sum, average, minimum or maximum. Transformation primitives, on the other hand, allow more complex manipulations such as normalisation or extraction of parts of a date.<\/p><p>It is the judicious use of these primitives that enables Featuretools to generate a wide range of features from existing data, without the user having to specify each operation manually.<\/p><p>This greatly simplifies the <strong>engineering process,<\/strong> making a variety of sophisticated operations accessible without the need for in-depth expertise.<\/p>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t<figure>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2023\/12\/Featuretools-3.jpg\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t<figcaption><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Discover our AI training courses<\/a><\/div><\/div>\n\n\t\t\t<h3>Predictive maintenance, marketing&#8230; an ideal tool for several use cases<\/h3>\t\t\n\t\t<p>It is also a particularly powerful tool for solving specific problems. For example, to predict the failure of industrial equipment, it can automatically extract <strong>complex temporal functionalities from sensor data.<\/strong><\/p><p>Similarly, in marketing or e-commerce, it can be used to create personalised features based on past customer behaviour. This improves the accuracy of <a href=\"https:\/\/liora.io\/en\/digital-analyst-unveiling-the-insights-behind-the-profession\">recommendation and segmentation models.<\/a><\/p>\t\t\n\t\t\t<h3>Integrating Featuretools into the Machine Learning workflow<\/h3>\t\t\n\t\t<p>As a <strong>machine learning tool<\/strong>, Featuretools is directly designed to integrate seamlessly with other popular libraries such as Pandas and <strong>Scikit-learn.<\/strong><\/p><p>This makes it possible to take advantage of its automation capabilities while continuing to use familiar tools, particularly for <strong>manipulating data or building and evaluating models.<\/strong><\/p><p>This ease of integration simplifies practitioners&#8217; transition to using Featuretools in their projects, without requiring a complete overhaul of their current workflow.<\/p><p>Compared to manual approaches to feature engineering, automation dramatically speeds up the process and reduces the potential for human error.<\/p>\t\t\n\t\t\t<h3>Conclusion: Featuretools, a real ally for Data Scientists<\/h3>\t\t\n\t\t<p>By <strong>automating the various stages of feature engineering, Featuretools<\/strong> saves precious time and increases the performance of Machine Learning models.<\/p><p>After just a few years, this<strong> Python library<\/strong> has established itself as one of the essential open source solutions for Machine Learning professionals.<\/p><p>To learn how to master <strong>Featuretools<\/strong> and all the best Machine Learning tools, you can choose Liora! Our range of distance learning courses will give you real expertise.<\/p><p>The Python language and Machine Learning are on the syllabus for our Data Scientist,<a href=\"https:\/\/liora.io\/en\/data-analyst-everything-you-need-to-know-about-the-job\"> Data Analyst<\/a> and Machine Learning Engineer courses. What&#8217;s more, the module dedicated to time series in our Deep Learning course covers pre-processing and feature engineering in detail.<\/p><p>Through these different programmes, you can discover all the techniques required to become a Data Science and AI professional, such as DataViz, Business Intelligence, databases, analysis methods, as well as putting ML models into production.<\/p><p>All our training courses are delivered remotely via BootCamp, on a part-time or continuous basis. Our organisation is <strong>eligible for funding options<\/strong>, and you can receive a state-recognised diploma and Cloud certification. Find out more about Liora!<\/p>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t<figure>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2023\/12\/Featuretools-2.jpg\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t<figcaption><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-scientist\">Find out more about our training courses<\/a><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Featuretools is an open source Python library created by Alteryx to automate feature engineering in Machine Learning. Find out everything you need to know about it: how it works, its benefits, use cases, etc. In the field of Machine Learning, the engineering of functionalities or features is an essential practice. The quality and relevance of [&hellip;]<\/p>\n","protected":false},"author":76,"featured_media":183469,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-183468","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/183468","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/76"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=183468"}],"version-history":[{"count":1,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/183468\/revisions"}],"predecessor-version":[{"id":205833,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/183468\/revisions\/205833"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/183469"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=183468"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=183468"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}