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<!DOCTYPE html> <html lang="en" xmlns:mml="http://www.w3.org/1998/Math/MathML"> <head> <meta charset="utf-8"> <meta http-equiv="x-ua-compatible" content="ie=edge"> <title>Super Learner</title> <!-- Preload Montserrat Fonts --> <link rel="preload" href="/assets/fonts/montserrat-v15-latin-ext_cyrillic-ext-regular.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="preload" href="/assets/fonts/montserrat-v25-latin-ext_cyrillic-ext-500.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="preload" href="/assets/fonts/montserrat-v15-latin-ext_cyrillic-ext-700.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <!-- Preload Merriweather Fonts --> <link rel="preload" href="/assets/fonts/merriweather-v30-latin-ext_cyrillic-ext-300.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="preload" href="/assets/fonts/merriweather-v22-latin-ext_cyrillic-ext-regular.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="preload" href="/assets/fonts/merriweather-v22-latin-ext_cyrillic-ext-700.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <!-- Preload Font Awesome --> <link rel="preload" href="/assets/stylesheets/fontawesome.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="stylesheet" media="all" href='/assets/stylesheets/02f295d1f6a8e9c282e7831cd493e8ff-bootstrap.purged.min.css' /> <link rel="stylesheet" media="all" href='/assets/stylesheets/5337b0511e7530e28f005276f70cde44-main.min.css' /> <link rel="stylesheet" media="all" href='/assets/stylesheets/13761c60ba5201f465803f3005ccd7ef-fontawesome-codes.css' /> <link rel="shortcut icon" type="image/x-icon" href='/assets/images/ec7d7606b4e2f3f921b5e1700948efb6-favicon.ico' /> <link rel="alternate" hreflang="en" href="https://www.degruyter.com/document/doi/10.2202/1544-6115.1309/html?lang=en" /> <link rel="alternate" hreflang="de" href="https://www.degruyter.com/document/doi/10.2202/1544-6115.1309/html?lang=de" /> <link rel="alternate" hreflang="x-default" href="https://www.degruyter.com/document/doi/10.2202/1544-6115.1309/html" /> <!--[if le IE 11]> <script nonce="C+LUmrHTMReisY5AgKmEcg==" src='/assets/javascripts/2568c6be22833eac0f750ff472b3caf0-polyfill.min.js'></script> <![endif]--> <link rel="schema.dcterms" href="http://purl.org/dc/terms/"> <meta name="dcterms.rightsHolder" content="Walter de Gruyter GmbH"> <meta name="dcterms.rights" content="De Gruyter expressly reserves the right to use all content for commercial text and data mining within the meaning of Section 44b of the German Copyright Act."> <link rel="dns-prefetch" href="https://www.google-analytics.com" /> <meta name="google" content="notranslate" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="description" content="When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many estimation procedures in their toolbox. A few examples of these candidate learners are: least squares, least angle regression, random forests, and spline regression. Previous articles (van der Laan and Dudoit (2003); van der Laan et al. (2006); Sinisi et al. (2007)) theoretically validated the use of cross validation to select an optimal learner among many candidate learners. Motivated by this use of cross validation, we propose a new prediction method for creating a weighted combination of many candidate learners to build the super learner. This article proposes a fast algorithm for constructing a super learner in prediction which uses V-fold cross-validation to select weights to combine an initial set of candidate learners. In addition, this paper contains a practical demonstration of the adaptivity of this so called super learner to various true data generating distributions. This approach for construction of a super learner generalizes to any parameter which can be defined as a minimizer of a loss function."/> <meta property="og:url" content="https://www.degruyter.com/document/doi/10.2202/1544-6115.1309/html"/><meta property="og:site_name" content="De Gruyter"/><meta property="og:title" content="Super Learner"/><meta property="og:type" content="article"/><meta property="og:locale" content="en"/><meta property="og:image" content="https://www.degruyter.com/document/cover/journal_key/SAGMB/product"/><meta property="og:image:type" content="image/jpeg"/><meta property="og:description" content="When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many estimation procedures in their toolbox. A few examples of these candidate learners are: least squares, least angle regression, random forests, and spline regression. Previous articles (van der Laan and Dudoit (2003); van der Laan et al. (2006); Sinisi et al. (2007)) theoretically validated the use of cross validation to select an optimal learner among many candidate learners. Motivated by this use of cross validation, we propose a new prediction method for creating a weighted combination of many candidate learners to build the super learner. This article proposes a fast algorithm for constructing a super learner in prediction which uses V-fold cross-validation to select weights to combine an initial set of candidate learners. In addition, this paper contains a practical demonstration of the adaptivity of this so called super learner to various true data generating distributions. This approach for construction of a super learner generalizes to any parameter which can be defined as a minimizer of a loss function."/><meta property="og:locale:alternate" content="de"/><meta property="article:author" content="Mark J. van der Laan"/><meta property="article:author" content="Eric C Polley"/><meta property="article:author" content="Alan E. Hubbard"/><meta property="article:tag" content="cross-validation"/><meta property="article:tag" content="loss-based estimation"/><meta property="article:tag" content="machine learning"/><meta property="article:tag" content="prediction"/><meta property="article:published_time" content="2007-09-16"/><meta property="article:section" content="Statistical Applications in Genetics and Molecular Biology"/> <meta name="citation_issue" content="1" /> <meta name="citation_issn" content="1544-6115" /> <meta name="citation_language" content='en' /> <meta name="citation_volume" content="6" /> <meta name="citation_publisher" content='De Gruyter' /> <meta name="citation_keywords" content='cross-validation; loss-based estimation; machine learning; prediction' /> <meta name="citation_author" content="Mark J. van der Laan" /> <meta name="citation_author" content="Eric C Polley" /> <meta name="citation_author" content="Alan E. 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Your documents are now available to view.</p> <div id="orderCompleteItemList"> </div> </div> </div> </div> </div> <main id="main" class='language_en min-vh-100 '> <div class="row no-theme-gutter-x g-0 " id="docContent"> <div class='offset-lg-1 col-lg-8 pb-2'> <div class='row g-0 '> <div class="col p-3 pt-5"> <div class="d-flex flex-wrap flex-column flex-md-row justify-content-between"> <div> <div class="mb-2"> <span class="d-none itemAuthorized"> <span class="fa fas fa-icon-authorized"></span> <span class="accessAccessible me-2">Licensed</span> </span> <span class="d-none itemNotAuthorized"> <span class="fa fas fa-icon-not-authorized"></span> <span class="d-none notAuthUserLoggedIn accessRequireAuth me-2">Unlicensed</span> <span class="d-none notAuthUserLoggedOut accessRequireAuth me-2">Requires Authentication</span> </span> <span class="publisherAndPublicationDate metadataInfoMainContent"> <span class="publisher"> Published by <a class="ga_published_by ga_published_by_header" href='/search?query=*&publisherFacet=De+Gruyter'>De Gruyter</a> </span> <span class="publicationDate">September 16, 2007</span> </span> </div> <div id="mobilePurchaseDiv" class="d-none"> <a class="mobilePurchaseButton" href="#"> Purchase article <svg width="17" height="16" viewBox="0 0 17 16" fill="none" xmlns="http://www.w3.org/2000/svg"> <path fill-rule="evenodd" clip-rule="evenodd" d="M12.75 10.3238L11.4513 9L9.5 11.1469L9.5 1L7.5 1L7.5 11.1473L5.54829 9L4.24962 10.3238L8.49981 15L12.75 10.3238Z" fill="#007596"/> </svg> </a> </div> <h1>Super Learner</h1> <ul class="contributors list-unstyled mb-2"> <li class="contributors-AUTHOR mb-2"> <span class="metadataAndContributorsFont"><span class="contributor"> <span class="displayName linkAnimation">Mark J. van der Laan</span> <contributor-popdown name="Mark J. van der Laan" position="1" email="" affiliations="" > </contributor-popdown> </span><span class="comma">, </span><span class="contributor"> <span class="displayName linkAnimation">Eric C Polley</span> <contributor-popdown name="Eric C Polley" position="2" email="" affiliations="" > </contributor-popdown> </span> and <span class="contributor"> <span class="displayName linkAnimation">Alan E. Hubbard</span> <contributor-popdown name="Alan E. Hubbard" position="3" email="" affiliations="" > </contributor-popdown> </span></span> </li> </ul> <div class="subTitleInfoProductPage">From the journal <a class="ga_parent ga_parent_journal" href="/journal/key/sagmb/html">Statistical Applications in Genetics and Molecular Biology</a></div> <div class="doi"><a href="https://doi.org/10.2202/1544-6115.1309" class="linkWithoutStyle subTitleInfoProductPage ga_doi" target="_blank">https://doi.org/10.2202/1544-6115.1309</a></div> </div> </div> </div> <div class="d-flex align-items-center flex-wrap px-4 px-lg-2 "> <button id='citationsModalButton' type="button" class="btn btn-main-content ga_cite_this me-2" href='#citationsModal' data-bs-toggle="modal" data-bs-target="#citationsModal" data-doi="10.2202/1544-6115.1309" aria-controls='citationsModal'> Cite this </button> <button id="socialModalButton" type="button" class="btn btn-main-content ga_share_this me-2" href="#socialModal" data-bs-toggle="modal" data-bs-target="#socialModal" aria-controls="socialModal"> Share this </button> <div class="dimensions __dimensions_badge_embed__ ga_dimensions_citation me-2" data-doi="10.2202/1544-6115.1309" data-hide-zero-citations="true" data-legend="never" data-style="large_rectangle"></div> </div> <div id="div-document-progress-bar" class="sticky-top d-none"> <div class="row"> <div class="col-12"> <div class="progress progress-bar-toolbar"> <div id="document-progress-bar" class="progress-bar bg-primary" role="progressbar" aria-label="Document progress bar" aria-valuenow="0" aria-valuemin="0" aria-valuemax="100"></div> </div> </div> </div> </div> <div id="document-main-content" class='row'> <div class="container-fluid px-3 px-lg-0 py-2"> <div class="col"> <div class="d-none analyticsHolder" data-subjects='LF|LF-01|LF-03|MT|MT-09' data-publisherCode='DEG' data-license='restricted' data-publisher='De Gruyter' data-contentName='Super Learner' data-doi='10.2202/1544-6115.1309' data-parentIdentifier='SAGMB' data-parentName='Statistical Applications in Genetics and Molecular Biology' data-languages='en' ></div> <div id="documentContent" class="content py-2" data-doi='10.2202/1544-6115.1309' data-accessrestricted="true" data-countertype="document"> <div class="px-2"> <div class="alert alert-info-box" role="alert"> <div id="noAccessReasonSpinner"> <div class="spinner-border text-primary" role="status"></div> </div> <div id="noAccessReasonContent"> <small class="fa fas fa-block"></small> <small id="noAccessReason"></small> <div class="previewAlertContainer"> <small class="previewAlert">Showing a limited preview of this publication:</small> </div> </div> </div> <div id="text-container"> <div xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" class="contentWrapper"><div class="article" lang="en"><div class="abstract"><p>When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many estimation procedures in their toolbox. A few examples of these candidate learners are: least squares, least angle regression, random forests, and spline regression. Previous articles (van der Laan and Dudoit (2003); van der Laan et al. (2006); Sinisi et al. (2007)) theoretically validated the use of cross validation to select an optimal learner among many candidate learners. Motivated by this use of cross validation, we propose a new prediction method for creating a weighted combination of many candidate learners to build the super learner. This article proposes a fast algorithm for constructing a super learner in prediction which uses V-fold cross-validation to select weights to combine an initial set of candidate learners. In addition, this paper contains a practical demonstration of the adaptivity of this so called super learner to various true data generating distributions. This approach for construction of a super learner generalizes to any parameter which can be defined as a minimizer of a loss function.</p></div><div class="keywords mb-3">Keywords: <a href="/search?query=keywordValues%3A%28%22cross-validation%22%29%20AND%20journalKey%3A%28%22SAGMB%22%29&documentVisibility=all&documentTypeFacet=article" class="ga_keyword">cross-validation</a>; <a href="/search?query=keywordValues%3A%28%22loss-based%20estimation%22%29%20AND%20journalKey%3A%28%22SAGMB%22%29&documentVisibility=all&documentTypeFacet=article" class="ga_keyword">loss-based estimation</a>; <a href="/search?query=keywordValues%3A%28%22machine%20learning%22%29%20AND%20journalKey%3A%28%22SAGMB%22%29&documentVisibility=all&documentTypeFacet=article" class="ga_keyword">machine learning</a>; <a href="/search?query=keywordValues%3A%28%22prediction%22%29%20AND%20journalKey%3A%28%22SAGMB%22%29&documentVisibility=all&documentTypeFacet=article" class="ga_keyword">prediction</a></div><div class="contrib-group"></div><div class="publicationDates mb-3"><div class="publishedOnlineDate"><span class="label-publishedOnline"><span><strong>Published Online: </strong></span></span>2007-9-16</div></div><div class="copyrightStatement"><p>©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston</p></div></div></div> </div> </div> </div> <div class="modal" id="citationsModal" tabindex="-1" role="dialog" aria-label='Cite this' aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-lg" role="document"> <div class="modal-content bg-white"> <div class="modal-body"> <button type="button" class="btn-close float-end btn-modal-close" data-bs-dismiss="modal" aria-label='Close'></button> <div id="citations"> <div class="row pb-2"> <div class="col"> <div class="text-dark fw-bold">Cite this article</div> </div> </div> <div class="row p-2"> <div class="col-lg-auto ps-4 mb-3 mb-lg-0"> <ul class="nav d-flex flex-lg-column" id="citationSelector" role="tablist"> <li class="nav-item py-1" role="presentation"> <a class="nav-link citations py-0 font-size-14 active" id="MLA-tab" data-bs-toggle="tab" href="#MLA" role="tab" aria-controls="MLA" aria-selected="true"> <span class="text-primary fa fas fa-ok me-2"></span> <span class="font-family-standard text-dark">MLA</span> </a> </li> <li class="nav-item py-1" role="presentation"> <a class="nav-link citations py-0 font-size-14 " id="APA-tab" data-bs-toggle="tab" href="#APA" role="tab" aria-controls="APA" aria-selected="true"> <span class="text-primary fa fas fa-ok me-2"></span> <span class="font-family-standard text-dark">APA</span> </a> </li> <li class="nav-item py-1" role="presentation"> <a class="nav-link citations py-0 font-size-14 " id="Harvard-tab" data-bs-toggle="tab" href="#Harvard" role="tab" aria-controls="Harvard" aria-selected="true"> <span class="text-primary fa fas fa-ok me-2"></span> <span class="font-family-standard text-dark">Harvard</span> </a> </li> <li class="nav-item py-1" role="presentation"> <a class="nav-link citations py-0 font-size-14 " id="Chicago-tab" data-bs-toggle="tab" href="#Chicago" role="tab" aria-controls="Chicago" aria-selected="true"> <span class="text-primary fa fas fa-ok me-2"></span> <span class="font-family-standard text-dark">Chicago</span> </a> </li> <li class="nav-item py-1" role="presentation"> <a class="nav-link citations py-0 font-size-14 " id="Vancouver-tab" data-bs-toggle="tab" href="#Vancouver" role="tab" aria-controls="Vancouver" aria-selected="true"> <span class="text-primary fa fas fa-ok me-2"></span> <span class="font-family-standard text-dark">Vancouver</span> </a> </li> </ul> </div> <div class="col"> <div class="tab-content border pt-4 px-4" id="citationContent"> <div class="tab-pane fade show active" id="MLA" role="tabpanel" aria-labelledby="MLA-tab">van der Laan, Mark J., Polley, Eric C and Hubbard, Alan E.. "Super Learner" <i>Statistical Applications in Genetics and Molecular Biology</i>, vol. 6, no. 1, 2007. <a href='https://doi.org/10.2202/1544-6115.1309'>https://doi.org/10.2202/1544-6115.1309</a></div> <div class="tab-pane fade " id="APA" role="tabpanel" aria-labelledby="APA-tab">van der Laan, M., Polley, E. & Hubbard, A. (2007). Super Learner. <i>Statistical Applications in Genetics and Molecular Biology</i>, <i>6</i>(1). <a href='https://doi.org/10.2202/1544-6115.1309'>https://doi.org/10.2202/1544-6115.1309</a></div> <div class="tab-pane fade " id="Harvard" role="tabpanel" aria-labelledby="Harvard-tab">van der Laan, M., Polley, E. and Hubbard, A. (2007) Super Learner. Statistical Applications in Genetics and Molecular Biology, Vol. 6 (Issue 1). <a href='https://doi.org/10.2202/1544-6115.1309'>https://doi.org/10.2202/1544-6115.1309</a></div> <div class="tab-pane fade " id="Chicago" role="tabpanel" aria-labelledby="Chicago-tab">van der Laan, Mark J., Polley, Eric C and Hubbard, Alan E.. "Super Learner" <i>Statistical Applications in Genetics and Molecular Biology</i> 6, no. 1 (2007). <a href='https://doi.org/10.2202/1544-6115.1309'>https://doi.org/10.2202/1544-6115.1309</a></div> <div class="tab-pane fade " id="Vancouver" role="tabpanel" aria-labelledby="Vancouver-tab">van der Laan M, Polley E, Hubbard A. 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