{"id":168529,"date":"2023-06-09T13:34:13","date_gmt":"2023-06-09T12:34:13","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=168529"},"modified":"2023-10-30T19:13:07","modified_gmt":"2023-10-30T18:13:07","slug":"data-leakage-definition-and-prevention","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/data-leakage-definition-and-prevention","title":{"rendered":"Data leakage: Understanding it and preventing it"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"168529\" class=\"elementor elementor-168529\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d9cff37 e-flex e-con-boxed e-con\" data-id=\"d9cff37\" 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-5960c43 elementor-widget elementor-widget-heading\" data-id=\"5960c43\" 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><h5 class=\"elementor-heading-title elementor-size-default\">Data leakage is a worrying phenomenon that can jeopardize your company's security. Find out how to protect your sensitive data against data leakage and computer attacks.<\/h5>\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-f83c857 e-flex e-con-boxed e-con\" data-id=\"f83c857\" 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-1c63b12 elementor-widget elementor-widget-heading\" data-id=\"1c63b12\" 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 Data Leakage ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7bcc69f elementor-widget elementor-widget-text-editor\" data-id=\"7bcc69f\" 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;\">Data leakage is one of the <\/span><b>most important points of vigilance<\/b><span style=\"font-weight: 400;\"> when designing a predictive model. The creation of a <\/span><b>predictive model<\/b><span style=\"font-weight: 400;\"> stems from an operational need, and the aim is to create a predictive tool to meet business expectations. <\/span><b>Performance<\/b><span style=\"font-weight: 400;\"> and <\/span><b>transparency<\/b><span style=\"font-weight: 400;\"> are the watchwords of a good <\/span><span style=\"font-weight: 400;\">predictive model<\/span><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Performance measurement is an <\/span><b>essential step<\/b><span style=\"font-weight: 400;\"> in model development, as it lies at the heart of the predictive modeling problem. It ensures the tool&#8217;s usability by guaranteeing its robustness, as it enables us to assess the operational character of the models. Indeed, the better a model performs, the more reliable and therefore usable it is. To assess its performance, we use metrics to <\/span><b>measure the quality of prediction<\/b><span style=\"font-weight: 400;\"> by comparing predicted values with actual values.<\/span><\/p><p><span style=\"font-weight: 400;\">During the design phase, we have a <\/span><b>certain amount of data<\/b><span style=\"font-weight: 400;\"> at our disposal. This data should enable us to both train and test the performance of our model. To obtain an accurate measure of performance, it is essential to have a <\/span><b>sufficient quantity of data<\/b><span style=\"font-weight: 400;\"> on which to test the model. This data must not be known to the model, and it must not be trained under any circumstances.<\/span><\/p><p><span style=\"font-weight: 400;\">For this stage to go as smoothly as possible, you need to be very rigorous in the <\/span><b>data preparation stage<\/b><span style=\"font-weight: 400;\">. Right from the start of the project, you must ensure that part of the data is set aside. If this is not done properly, data not intended for training could leak out and be used to train the model. This would then <\/span><b>bias the results<\/b><span style=\"font-weight: 400;\"> of the model when evaluated. This is what is known as data leakage in <a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\">Machine Learning<\/a>.<\/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-fe6faec elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"fe6faec\" 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=\"741\" height=\"427\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/machine-learning_1.jpg\" class=\"attachment-large size-large wp-image-168536\" alt=\"machine-learning\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/machine-learning_1.jpg 741w, https:\/\/datascientest.com\/en\/files\/2023\/06\/machine-learning_1-300x173.jpg 300w\" sizes=\"(max-width: 741px) 100vw, 741px\" \/>\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-4dffb6a elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"4dffb6a\" 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\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn how to effectively manage data leakage<\/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-9c6ea88 e-flex e-con-boxed e-con\" data-id=\"9c6ea88\" 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-3e90ad2 elementor-widget elementor-widget-heading\" data-id=\"3e90ad2\" 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 determine if there has been a data leak?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-88c4205 elementor-widget elementor-widget-text-editor\" data-id=\"88c4205\" 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 very good indicator is abnormally high model performance. Getting a very high score for a model that predicts a customer&#8217;s subscription, for example, sports predictions, should give us a red flag. It&#8217;s virtually<\/span><b> impossible to obtain very high scores<\/b><span style=\"font-weight: 400;\"> on issues such as these, as the degree of chance involved in the realization of an event is very high. So we need to take a step back from the results we&#8217;ve obtained and take care to<\/span><b> check<\/b><span style=\"font-weight: 400;\"> how we arrived at that score.<\/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-44a72c9 e-flex e-con-boxed e-con\" data-id=\"44a72c9\" 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-6414cd2 elementor-widget elementor-widget-heading\" data-id=\"6414cd2\" 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 precautions should be taken?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86e9374 elementor-widget elementor-widget-text-editor\" data-id=\"86e9374\" 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 <\/span><b>train-test split technique<\/b><span style=\"font-weight: 400;\"> (also known as hold-out) involves dividing the available data into two parts: one dedicated to training and the other to evaluation. Only after the model has been trained can the test data be consulted; before this stage, they must have been carefully set aside.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">As previously mentioned, it&#8217;s only after this crucial data separation stage has been completed that we can proceed with <\/span><b>data preparation <\/b><span style=\"font-weight: 400;\">(the preprocessing phase). During this stage, we decide which treatments are to be applied to our variables before training the chosen algorithm.<\/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-6769154 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"6769154\" 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=\"800\" height=\"489\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/data-leakage_1.jpg\" class=\"attachment-large size-large wp-image-168537\" alt=\"data-leakage\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/data-leakage_1.jpg 830w, https:\/\/datascientest.com\/en\/files\/2023\/06\/data-leakage_1-300x183.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/data-leakage_1-768x469.jpg 768w\" 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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bb5de92 e-flex e-con-boxed e-con\" data-id=\"bb5de92\" 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-00cd630 elementor-widget elementor-widget-heading\" data-id=\"00cd630\" 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\">Why is it not possible to use all the available data?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-986df42 elementor-widget elementor-widget-text-editor\" data-id=\"986df42\" 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;\">To better understand how this works, let&#8217;s take a look at the imputation of the missing values phase. Let&#8217;s imagine that we want to impute all the missing values of a variable by its median. If we calculate the median on all the data (training and test sets combined), then the value of the median will be different from that calculated only on the training set. This will result in a data leak, as the position indicator contains information from the test set. This example extends, of course, to all the pre-processing steps that precede model training: imputation of missing values, treatment of extreme values, normalization, etc.<\/span><\/p><p><span style=\"font-weight: 400;\">Of course, this precaution also applies to the cross-validation technique. Validation sets must be set aside to remain unknown to the model.<\/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-74936f4 e-flex e-con-boxed e-con\" data-id=\"74936f4\" 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-7717b27 elementor-widget elementor-widget-heading\" data-id=\"7717b27\" 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\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1854299 elementor-widget elementor-widget-text-editor\" data-id=\"1854299\" 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;\">Performance is largely determined by the quality of the data, so it is important to ensure that they are prepared before training the model. Nevertheless, this is a delicate stage, as it is prone to data leakage. Great care must be taken to ensure that no information contained in the test set is used to train the model. To ensure a model&#8217;s true performance, we can only rely on this approach.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">If you&#8217;d like to find out more about model prediction, don&#8217;t hesitate to read <\/span><a href=\"https:\/\/datascientest.com\/en\/data-warehouse-2\"><span style=\"font-weight: 400;\">our article on data warehouse<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"https:\/\/datascientest.com\/en\/database-what-is-it\"><span style=\"font-weight: 400;\">our article on databases<\/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<div class=\"elementor-element elementor-element-f6705f0 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"f6705f0\" 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\/data-scientist-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\">Discover the Data Scientist 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-5cd9f3a e-flex e-con-boxed e-con\" data-id=\"5cd9f3a\" 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-d808290 elementor-widget elementor-widget-html\" data-id=\"d808290\" 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 Data Leakage ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Data leakage is one of the most important points of vigilance when designing a predictive model. The creation of a predictive model stems from an operational need, and the aim is to create a predictive tool to meet business expectations. Performance and transparency are the watchwords of a good predictive model.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"How can I determine if there has been a data leak?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"A very good indicator is abnormally high model performance. Getting a very high score for a model that predicts a customer's subscription, for example, sports predictions, should give us a red flag. It's virtually impossible to obtain very high scores on issues such as these, as the degree of chance involved in the realization of an event is very high. So we need to take a step back from the results we've obtained and take care to check how we arrived at that score.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Why is it not possible to use all the available data?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"To better understand how this works, let's take a look at the imputation of the missing values phase. Let's imagine that we want to impute all the missing values of a variable by its median. If we calculate the median on all the data (training and test sets combined), then the value of the median will be different from that calculated only on the training set. This will result in a data leak, as the position indicator contains information from the test set. This example extends, of course, to all the pre-processing steps that precede model training: imputation of missing values, treatment of extreme values, normalization, etc.\"\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>Data leakage is a worrying phenomenon that can jeopardize your company&#8217;s security. Find out how to protect your sensitive data against data leakage and computer attacks. What is Data Leakage ? Data leakage is one of the most important points of vigilance when designing a predictive model. The creation of a predictive model stems from [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":168530,"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>Data leakage: Understanding it and preventing it | DataScientest<\/title>\n<meta name=\"description\" content=\"Data leakage is a worrying phenomenon that can jeopardize your company&#039;s security. 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