{"id":167160,"date":"2023-03-09T11:50:30","date_gmt":"2023-03-09T10:50:30","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=167160"},"modified":"2024-01-29T18:42:54","modified_gmt":"2024-01-29T17:42:54","slug":"shap-what-is-it","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/shap-what-is-it","title":{"rendered":"SHapley Additive exPlanations or SHAP : What is it ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"167160\" class=\"elementor elementor-167160\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-65b377f e-flex e-con-boxed e-con\" data-id=\"65b377f\" 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-419885e elementor-widget elementor-widget-text-editor\" data-id=\"419885e\" 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;\">SHapley Additive exPlanations, more commonly known as SHAP, is used to <\/span><b>explain the output of Machine Learning<\/b><span style=\"font-weight: 400;\"> models. It is based on Shapley values, which use game theory to assign credit for a model&#8217;s prediction to each feature or feature value.<\/span><\/p><p><span style=\"font-weight: 400;\">The way SHAP works is to <\/span><b>decompose the output <\/b><span style=\"font-weight: 400;\">of a model by the sums of the impact of each feature. SHAP calculates a value that represents the contribution of each feature to the <\/span><b>model outcome<\/b><span style=\"font-weight: 400;\">. These values can be used to understand the<\/span><b> importance of each feature<\/b><span style=\"font-weight: 400;\"> and to explain the result of the model to a human. This is especially valuable for agencies and teams that report to their clients or managers.<\/span><\/p><p><span style=\"font-weight: 400;\">SHAP has <\/span><b>several interesting properties<\/b><span style=\"font-weight: 400;\">, such as its neutrality towards models. This allows it to be used on any learning model, to <\/span><b>produce consistent explanations<\/b><span style=\"font-weight: 400;\">, and to handle complex model behaviors (when features interact with each other, for example).<\/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-6e04627 e-flex e-con-boxed e-con\" data-id=\"6e04627\" 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-942025d elementor-widget elementor-widget-heading\" data-id=\"942025d\" 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\">WHat SHAP is it used for ? <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a604ed4 elementor-widget elementor-widget-text-editor\" data-id=\"a604ed4\" 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;\">SHAP has many uses for data science professionals. First, it helps <\/span><b>explain the predictions<\/b><span style=\"font-weight: 400;\"> of <\/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;\"> in a way that humans can understand. By assigning a value to each input feature, it shows how and to what extent each feature contributed to the <\/span><b>final prediction result<\/b><span style=\"font-weight: 400;\">. This way, the team can understand how the model made its decision and can identify the most important features.<\/span><\/p><p><span style=\"font-weight: 400;\">As explained earlier, this model is called agnostic (neutral). It can be <\/span><b>used with any Machine Learning model<\/b><span style=\"font-weight: 400;\">. So you don&#8217;t have to worry about the structure of the model to understand the prediction result with SHAP. Moreover, this model is consistent. You can therefore trust the explanations produced, regardless of the model studied.<\/span><\/p><p><span style=\"font-weight: 400;\">Finally, SHAP is particularly useful for handling complex behaviors. You can use this technique to understand how different features affect the model prediction together.<\/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-f385b7f elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"f385b7f\" 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=\"745\" height=\"419\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/image2.png\" class=\"attachment-large size-large wp-image-167175\" alt=\"SHAP2\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/image2.png 745w, https:\/\/datascientest.com\/en\/files\/2023\/03\/image2-300x169.png 300w\" sizes=\"(max-width: 745px) 100vw, 745px\" \/>\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-5e52be1 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"5e52be1\" 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\"> Discover our training of Machine LEarning Engineer <\/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-2e8c6d8 e-flex e-con-boxed e-con\" data-id=\"2e8c6d8\" 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-687a0ca elementor-widget elementor-widget-heading\" data-id=\"687a0ca\" 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 to use SHAP to explain predictions?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4fd710d elementor-widget elementor-widget-text-editor\" data-id=\"4fd710d\" 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;\">Here is how to use SHAP to explain the predictions of a Machine Learning model:<\/span><\/p><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Install the SHAP package using &#8216;pip install shap&#8217;.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Import the SHAP package and other necessary libraries, such as <\/span><b>Numpy<\/b><span style=\"font-weight: 400;\"> and <\/span><b>Matplotlib<\/b><span style=\"font-weight: 400;\">.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Load your <\/span><b>Machine Learning model <\/b><span style=\"font-weight: 400;\">and prepare the input data you want to explain.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Create a SHAP object using the &#8216;<\/span><b>shap.TreeExplainer<\/b><span style=\"font-weight: 400;\">&#8216; function for tree-based models, or &#8216;<\/span><b>shap.KernelExplainer<\/b><span style=\"font-weight: 400;\">&#8216; for other model types.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Call the &#8216;<\/span><b>explain<\/b><span style=\"font-weight: 400;\">&#8216; method of the SHAP object by passing it the input data you want to explain. This method will <\/span><b>return a matrix of SHAP values<\/b><span style=\"font-weight: 400;\"> that represents the impact of each feature on the model prediction.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Use the SHAP values to visualize and interpret the results. For example, you can use the &#8216;<\/span><b>shap.summary_plot<\/b><span style=\"font-weight: 400;\">&#8216; function to generate a summary graph that shows the relative importance of each feature. You can also use the &#8216;<\/span><b>shap.dependence_plot<\/b><span style=\"font-weight: 400;\">&#8216; function to visualize how a particular feature influences the prediction of the model as a function of the value of that feature.<\/span><\/li><\/ol><p>\u00a0<\/p><p><span style=\"font-weight: 400;\">This technique is simple and very efficient.<\/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-5e55ded e-flex e-con-boxed e-con\" data-id=\"5e55ded\" 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-3240482 elementor-widget elementor-widget-heading\" data-id=\"3240482\" 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\">Example of SHAP use<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-261e069 elementor-widget elementor-widget-text-editor\" data-id=\"261e069\" 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 will find below an example of SHAP use, based on decision trees. To better understand the example, let&#8217;s talk about <\/span><b>TreeExplainer<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">TreeExplainer uses an <\/span><b>approach based on the approximation of the set of trees<\/b><span style=\"font-weight: 400;\"> to calculate the SHAP values of each characteristic. It is therefore useful for explaining predictions of Machine Learning models<\/span><b> using decision trees<\/b><span style=\"font-weight: 400;\">. It is also useful for explaining regression and classification models, Random Forests, and Gradient Boosting Machines.<\/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-88873a0 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"88873a0\" 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=\"450\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/image1.png\" class=\"attachment-large size-large wp-image-167176\" alt=\"Random forest\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/03\/image1.png 800w, https:\/\/datascientest.com\/en\/files\/2023\/03\/image1-300x169.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/03\/image1-768x432.png 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<div class=\"elementor-element elementor-element-f26b957 elementor-widget elementor-widget-text-editor\" data-id=\"f26b957\" 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;\">Here is a simple example of using SHAP with a regression model based on decision trees:<\/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-6f88645 elementor-widget elementor-widget-code-highlight\" data-id=\"6f88645\" data-element_type=\"widget\" data-widget_type=\"code-highlight.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"prismjs-default copy-to-clipboard \">\n\t\t\t<pre data-line=\"\" class=\"highlight-height language-javascript line-numbers\">\n\t\t\t\t<code readonly=\"true\" class=\"language-javascript\">\n\t\t\t\t\t<xmp>import shap\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n# Load your model\r\nmodel = load_model()\r\n\r\n# Prepare entry-data you want to explain \r\nX = prepare_data()\r\n\r\n# Create a SHAP object by using TreeExplainer \r\nexplainer = shap.TreeExplainer(model)\r\n\r\n# Call the explain method by using entry data\r\nshap_values = explainer.explain(X)\r\n\r\n# Display a summary graph of the relative importance of each characteristic\r\nshap.summary_plot(shap_values, X)\r\n\r\n# Display the dependency diagram of the characteristic \"age\u201d\r\nshap.dependence_plot(\u00ab age \u00bb, shap_values, X)<\/xmp>\n\t\t\t\t<\/code>\n\t\t\t<\/pre>\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-8bd075d elementor-widget elementor-widget-text-editor\" data-id=\"8bd075d\" 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;\">This example calculates the <\/span><b>SHAP values for each X element<\/b><span style=\"font-weight: 400;\">. It then displays a summary graph of the relative importance of each feature and a dependency plot for the age category.<\/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-d1eb3c8 e-flex e-con-boxed e-con\" data-id=\"d1eb3c8\" 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-8361b69 elementor-widget elementor-widget-heading\" data-id=\"8361b69\" 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\">In conclusion\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-296026f elementor-widget elementor-widget-text-editor\" data-id=\"296026f\" 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;\">SHAP is thus a technique that allows to explain the <\/span><b>predictions of Machine Learning models<\/b><span style=\"font-weight: 400;\"> in a versatile and powerful way. This method is agnostic, consistent, and can <\/span><b>handle complex model behavior<\/b><span style=\"font-weight: 400;\">. SHAP is particularly useful for understanding how a model works, identifying important features, and explaining the <\/span><b>result of predictions<\/b><span style=\"font-weight: 400;\"> to others on your team or to your customers.<\/span><\/p><p><span style=\"font-weight: 400;\">Now that you&#8217;ve discovered SHAP, you may want to master it. To do so, we invite you to learn more about <\/span><a href=\"https:\/\/datascientest.com\/en\/data-courses\"><b>DataScientest training courses<\/b><\/a><span style=\"font-weight: 400;\"> that incorporate Machine Learning into the curriculum.<\/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-d566992 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"d566992\" 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 course<\/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-73987e4 e-flex e-con-boxed e-con\" data-id=\"73987e4\" 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-195b35f elementor-widget elementor-widget-html\" data-id=\"195b35f\" 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 SHAP is it used for ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"SHAP has many uses for data science professionals. First, it helps explain the predictions of Machine Learning models in a way that humans can understand. By assigning a value to each input feature, it shows how and to what extent each feature contributed to the final prediction result. This way, the team can understand how the model made its decision and can identify the most important features.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"How to use SHAP to explain predictions?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Install the SHAP package using 'pip install shap'\"\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>SHapley Additive exPlanations, more commonly known as SHAP, is used to explain the output of Machine Learning models. It is based on Shapley values, which use game theory to assign credit for a model&#8217;s prediction to each feature or feature value. The way SHAP works is to decompose the output of a model by the [&hellip;]<\/p>\n","protected":false},"author":50,"featured_media":167162,"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>SHapley Additive exPlanations or SHAP : What is it ?<\/title>\n<meta name=\"description\" content=\"SHapley Additive exPlanations, more commonly known as SHAP, is used to explain the output of Machine Learning models.\" \/>\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\/shap-what-is-it\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SHapley Additive exPlanations or SHAP : What is it ?\" \/>\n<meta property=\"og:description\" content=\"SHapley Additive exPlanations, more commonly known as SHAP, is used to explain the output of Machine Learning models. 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