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What is k-Nearest Neighbor (kNN)? | A Comprehensive k-Nearest Neighbor Guide | Elastic
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form.fallback{position:relative;text-align:left;max-width:100%;}#fallback-form form.fallback .input-wrapper input{width:100%;outline:none;}#fallback-form form.fallback .input-wrapper input[type=text],#fallback-form form.fallback .input-wrapper input[type=email],#fallback-form form.fallback .input-wrapper input[type=number],#fallback-form form.fallback .input-wrapper input[type=tel],#fallback-form form.fallback .input-wrapper input[type=url],#fallback-form form.fallback .input-wrapper select{width:100% !important;color:var(--color-ink);height:50px;max-height:50px;line-height:40px !important;min-width:190px;padding-left:8px !important;padding-right:8px !important;font-size:14px;font-weight:400;border-radius:0;border:1px solid var(--color-dark-gray);}#fallback-form form.fallback .input-wrapper input[type=text]:focus,#fallback-form form.fallback .input-wrapper input[type=email]:focus,#fallback-form form.fallback .input-wrapper input[type=number]:focus,#fallback-form form.fallback 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input[type=tel].mktoInvalid:focus,#fallback-form form.fallback .input-wrapper input[type=url].mktoInvalid:focus,#fallback-form form.fallback .input-wrapper select.mktoInvalid:focus{border-bottom:2px solid var(--color-elastic-blue);}#fallback-form form.fallback .input-wrapper textarea{border:1px solid var(--color-dark-gray);height:4.5em;width:100% !important;margin-bottom:32px;}#fallback-form form.fallback .input-wrapper textarea:focus{outline:none;border-bottom:2px solid var(--color-elastic-blue);}#fallback-form form.fallback .input-wrapper textarea.mktoInvalid{border-bottom:2px solid var(--color-dark-orange);}#fallback-form form.fallback .input-wrapper textarea.mktoInvalid:focus{border-bottom:2px solid var(--color-elastic-blue);}#fallback-form form.fallback .input-wrapper input[type=checkbox]{height:auto !important;position:relative;width:16px !important;}#fallback-form form.fallback .input-wrapper 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.submit-form:hover{background:var(--color-dark-blue);border-color:var(--color-dark-blue);}#fallback-form form.fallback .submit-form:active{border-color:var(--color-light-blue);}#fallback-form form.fallback .submit-form:focus{box-shadow:none;}.mkto-form-wrapper.long-form #marketo-fe-form .mktoForm .mktoFieldWrap,.marketo-form.long-form #marketo-fe-form .mktoForm .mktoFieldWrap{margin-bottom:24px;}.mkto-form-wrapper.long-form #marketo-fe-form .mktoForm .mktoFieldWrap input[type=text],.mkto-form-wrapper.long-form #marketo-fe-form .mktoForm .mktoFieldWrap input[type=email],.mkto-form-wrapper.long-form #marketo-fe-form .mktoForm .mktoFieldWrap input[type=number],.mkto-form-wrapper.long-form #marketo-fe-form .mktoForm .mktoFieldWrap input[type=tel],.mkto-form-wrapper.long-form #marketo-fe-form .mktoForm .mktoFieldWrap input[type=url],.marketo-form.long-form #marketo-fe-form .mktoForm .mktoFieldWrap input[type=text],.marketo-form.long-form #marketo-fe-form .mktoForm .mktoFieldWrap 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stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/community" tabindex="-1">Join our community<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul></div></div></div><div class="Items_Section__vJ9lm"><div class=" Card_nav-card__Gb2bd Card_icon-card__IEL2U "><div class="Card_card-header__dLhyK"><div class="Card_card-image__34JYo"><div class=" Card_icon__VSCRq "><img src="https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/bltdb0f38c35ae455dc/5d07f086877575d0584760a3/logo-cloud-24-color.svg" class="Card_img-fluid__EqrlH" alt=""/></div></div><h2 class="h5">Elastic Cloud</h2></div><div class="Card_card-body__81rqO"><div><p>Unlock the power of real-time insights with Elastic on your preferred cloud provider.</p></div><div class="d-flex flex-column"><a class="button Card_dropdown-link__qZWTu Card_dropdown-link__spaced___Q1m0 Card_btn-tertiary__f8UEk btn-tertiary" href="/cloud" tabindex="-1"><span class="jsx-1596590093 btn-copy">Elastic Cloud overview</span><svg class="icon-embed" width="27" height="15"><use href="/static-res/images/generic-icons.svg#cta-arrow"></use></svg></a></div><hr class="Card_nav-divider__yt_19"/><ul><li><a class="Card_dropdown-link__qZWTu" href="/cloud/serverless" tabindex="-1">Elastic Cloud Serverless<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/pricing/serverless-search" tabindex="-1">Elastic Cloud Serverless pricing<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path 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class="Card_custom-link__semnr" href="/generative-ai"></a><a class="Card_custom-link__semnr" href="/generative-ai"></a></div><div class="Card_card-body__81rqO"><div><p>Prototype and integrate with LLMs faster using search AI.</p></div><ul><li><a class="Card_dropdown-link__qZWTu" href="/cloud/serverless/search-ai-lake" tabindex="-1">Search AI Lake<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/elasticsearch/ai-assistant" tabindex="-1">Elastic AI Assistant<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/enterprise-search/rag" tabindex="-1">Retrieval Augmented Generation<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul><hr class="Card_nav-divider__yt_19"/><ul><li><a class="Card_dropdown-link__qZWTu" href="/blog/category/generative-ai" tabindex="-1">Generative AI blogs<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="https://www.elastic.co/search-labs/tutorials" tabindex="-1">Search Labs tutorials<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 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class="Card_custom-link__semnr" href="/enterprise-search"><div class="Card_card-image__34JYo"><div class=" Card_icon__VSCRq "><img src="https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/blt549f7d977c2a88f4/5d082d34616162aa5a85707d/logo-enterprise-search-32-color.svg" class="Card_img-fluid__EqrlH" alt=""/></div></div></a><a class="Card_custom-link__semnr" href="/enterprise-search"><h2 class="h5">Search</h2></a><a class="Card_custom-link__semnr" href="/enterprise-search"></a><a class="Card_custom-link__semnr" href="/enterprise-search"></a><a class="Card_custom-link__semnr" href="/enterprise-search"></a><a class="Card_custom-link__semnr" href="/enterprise-search"></a></div><div class="Card_card-body__81rqO"><div><p>Discover a world of AI possibilities — built with the power of search.</p></div><ul><li><a class="Card_dropdown-link__qZWTu" href="/elasticsearch/vector-database" tabindex="-1">Vector database<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/enterprise-search/relevance" tabindex="-1">Relevance<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/enterprise-search/search-applications" tabindex="-1">Search applications<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/enterprise-search/ecommerce" tabindex="-1">Ecommerce<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/enterprise-search/site-search" tabindex="-1">Website search<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/enterprise-search/workplace-search" tabindex="-1">Workplace search<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/enterprise-search/customer-support" tabindex="-1">Customer support<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul><hr class="Card_nav-divider__yt_19"/><ul><li><a class="Card_dropdown-link__qZWTu" href="https://www.elastic.co/search-labs" tabindex="-1">Search Labs<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul></div><div class="Card_card-footer__OfpS8"><div class="d-flex flex-column"><a class="button Card_dropdown-link__qZWTu Card_dropdown-link__spaced___Q1m0 Card_btn-tertiary__f8UEk btn-tertiary" href="/enterprise-search" tabindex="-1"><span class="jsx-1596590093 btn-copy">Search overview</span><svg class="icon-embed" width="27" height="15"><use href="/static-res/images/generic-icons.svg#cta-arrow"></use></svg></a></div></div></div><div class=" Card_nav-card__Gb2bd Card_icon-card__IEL2U "><div class="Card_card-header__dLhyK"><a class="Card_custom-link__semnr" href="/security"><div class="Card_card-image__34JYo"><div class=" Card_icon__VSCRq "><img src="https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/bltf58b7c8e04706979/5e20f1a8132ead1155e8d0a4/logo-security-32-color.svg" class="Card_img-fluid__EqrlH" alt=""/></div></div></a><a class="Card_custom-link__semnr" href="/security"><h2 class="h5">Security</h2></a><a class="Card_custom-link__semnr" href="/security"></a><a class="Card_custom-link__semnr" href="/security"></a><a class="Card_custom-link__semnr" href="/security"></a><a class="Card_custom-link__semnr" href="/security"></a></div><div class="Card_card-body__81rqO"><div><p>Protect, investigate, and respond to cyber threats with AI-driven security analytics.</p></div><ul><li><a class="Card_dropdown-link__qZWTu" href="/security/siem" tabindex="-1">SIEM<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/security/ai" tabindex="-1">AI for the SOC<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/security/cyber-threat-research" tabindex="-1">Threat Research<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul><hr class="Card_nav-divider__yt_19"/><ul><li><a class="Card_dropdown-link__qZWTu" href="https://www.elastic.co/security-labs" tabindex="-1">Security Labs<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul></div><div class="Card_card-footer__OfpS8"><div class="d-flex flex-column"><a class="button Card_dropdown-link__qZWTu Card_dropdown-link__spaced___Q1m0 Card_btn-tertiary__f8UEk btn-tertiary" href="/security" tabindex="-1"><span class="jsx-1596590093 btn-copy">Security overview</span><svg class="icon-embed" width="27" height="15"><use href="/static-res/images/generic-icons.svg#cta-arrow"></use></svg></a></div></div></div><div class=" Card_nav-card__Gb2bd Card_icon-card__IEL2U "><div class="Card_card-header__dLhyK"><a class="Card_custom-link__semnr" href="/observability"><div class="Card_card-image__34JYo"><div class=" Card_icon__VSCRq "><img src="https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/bltbf6ba0d0e0e1e5ab/5e20f19a2aa8e40a75136318/logo-observability-32-color.svg" class="Card_img-fluid__EqrlH" alt=""/></div></div></a><a class="Card_custom-link__semnr" href="/observability"><h2 class="h5">Observability</h2></a><a class="Card_custom-link__semnr" href="/observability"></a><a class="Card_custom-link__semnr" href="/observability"></a><a class="Card_custom-link__semnr" href="/observability"></a><a class="Card_custom-link__semnr" href="/observability"></a></div><div class="Card_card-body__81rqO"><div><p>Unify app and infrastructure visibility to proactively resolve issues.</p></div><ul><li><a class="Card_dropdown-link__qZWTu" href="/observability/log-monitoring" tabindex="-1">Log monitoring and analytics<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" 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27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/observability/synthetic-monitoring" tabindex="-1">Synthetic monitoring<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/observability/real-user-monitoring" tabindex="-1">Real user monitoring<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/observability/universal-profiling" tabindex="-1">Universal Profiling<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/observability/aiops" tabindex="-1">AIOps<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul><hr class="Card_nav-divider__yt_19"/><ul><li><a class="Card_dropdown-link__qZWTu" href="https://www.elastic.co/observability-labs" tabindex="-1">Observability Labs<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul></div><div class="Card_card-footer__OfpS8"><div class="d-flex flex-column"><a class="button Card_dropdown-link__qZWTu Card_dropdown-link__spaced___Q1m0 Card_btn-tertiary__f8UEk btn-tertiary" href="/observability" tabindex="-1"><span class="jsx-1596590093 btn-copy">Observability overview</span><svg class="icon-embed" width="27" height="15"><use href="/static-res/images/generic-icons.svg#cta-arrow"></use></svg></a></div></div></div></div></div></div></div></div><div><button class="Items_navigation-items_item__7YSef navigation-item" aria-label="Customers, press down to open menu." aria-expanded="false">Customers<svg class="icons_icon__n3nWe" xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" viewBox="0 0 16 16"><path fill-rule="evenodd" d="M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z"></path></svg></button><div class="Dropdown_Dropdown__pUJPf 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src="https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/bltacc5e178e52f430e/647ccc7b6be35fb5eabd2100/icon-checkmark-decorative-border.svg" class="Card_img-fluid__EqrlH" alt=""/></div></div></a><a class="Card_custom-link__semnr" href="/customers"><h2 class="h5">By solution</h2></a><a class="Card_custom-link__semnr" href="/customers"></a><a class="Card_custom-link__semnr" href="/customers"></a></div><div class="Card_card-body__81rqO"><div><p>See how customers search, solve, and succeed — all on one Search AI Platform.</p></div><ul><li><a class="Card_dropdown-link__qZWTu" href="/customers/success-stories?usecase=enterprise-search&industry=All" tabindex="-1">Search<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/customers/success-stories?usecase=security-analytics&industry=All" tabindex="-1">Security<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/customers/success-stories?usecase=elastic-observability&industry=All" tabindex="-1">Observability<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul></div><div class="Card_card-footer__OfpS8"><div class="d-flex flex-column"><a class="button Card_dropdown-link__qZWTu Card_dropdown-link__spaced___Q1m0 Card_btn-tertiary__f8UEk btn-tertiary" href="/customers" tabindex="-1"><span class="jsx-1596590093 btn-copy">All customer stories</span><svg class="icon-embed" width="27" height="15"><use href="/static-res/images/generic-icons.svg#cta-arrow"></use></svg></a></div></div></div><div class=" Card_nav-card__Gb2bd Card_icon-card__IEL2U "><div class="Card_card-header__dLhyK"><a class="Card_custom-link__semnr" href="/industries"><div class="Card_card-image__34JYo"><div class=" Card_icon__VSCRq "><img src="https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/blt9f634b6f74878698/647ccc625637890f19859454/icon-briefcase.svg" class="Card_img-fluid__EqrlH" alt=""/></div></div></a><a class="Card_custom-link__semnr" href="/industries"><h2 class="h5">Industries</h2></a><a class="Card_custom-link__semnr" href="/industries"></a><a class="Card_custom-link__semnr" href="/industries"></a></div><div class="Card_card-body__81rqO"><div><p>Exceed customer expectations and go to market faster.</p></div><ul><li><a class="Card_dropdown-link__qZWTu" href="/industries/public-sector" tabindex="-1">Public sector<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/industries/financial-services" tabindex="-1">Financial services<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/industries/telecommunications" tabindex="-1">Telecommunications<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/industries/retail-ecommerce" tabindex="-1">Retail<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li><li><a class="Card_dropdown-link__qZWTu" href="/industries/manufacturing" tabindex="-1">Manufacturing<svg width="27" height="14" viewBox="0 0 27 14" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 7H25" stroke="#0B64DD" stroke-width="2"></path><path d="M19 1L25 7L19 13" stroke="#0B64DD" stroke-width="2"></path></svg></a></li></ul></div><div class="Card_card-footer__OfpS8"><div class="d-flex flex-column"><a class="button Card_dropdown-link__qZWTu Card_dropdown-link__spaced___Q1m0 Card_btn-tertiary__f8UEk btn-tertiary" href="/industries" tabindex="-1"><span class="jsx-1596590093 btn-copy">Industries overview</span><svg class="icon-embed" width="27" height="15"><use href="/static-res/images/generic-icons.svg#cta-arrow"></use></svg></a></div></div></div></div><div class="Items_Section__vJ9lm Items_spotlight-feature___wj__"><div class=" Card_nav-card__Gb2bd undefined "><div class="Card_card-header__dLhyK"><h2 class="Card_topic-heading___KyPp">Customer spotlight</h2></div><div><div class="Spotlight_spotlight-card__Qi_WV"><div class="Spotlight_card-image__4S6cS"><img src="https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/blt652fec920f2b1a14/650c1d87aa86c13fa1cc288c/logo-nav-dropdown-48x48-cisco.svg" alt="" class=""/></div><div class="Spotlight_card-body___rb9E"><p>Cisco saves 5,000 support engineer hours per month</p><a class="button Spotlight_stretched-link__2M98R btn-tertiary" href="/customers/cisco" tabindex="-1"><span class="jsx-1596590093 btn-copy">Read more</span><svg class="icon-embed" width="27" height="15"><use href="/static-res/images/generic-icons.svg#cta-arrow"></use></svg></a></div></div><div class="Spotlight_spotlight-card__Qi_WV"><div class="Spotlight_card-image__4S6cS"><img src="https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/blte788c0f923f209a3/668c49f5534bb913eb26e9e8/logo-dropdown-48x48-sitecore.svg" alt="" class=""/></div><div class="Spotlight_card-body___rb9E"><p>Sitecore automates 96 percent of security workflows with Elastic</p><a class="button Spotlight_stretched-link__2M98R btn-tertiary" href="/customers/sitecore-security" tabindex="-1"><span class="jsx-1596590093 btn-copy">Read more</span><svg class="icon-embed" width="27" height="15"><use href="/static-res/images/generic-icons.svg#cta-arrow"></use></svg></a></div></div><div class="Spotlight_spotlight-card__Qi_WV"><div class="Spotlight_card-image__4S6cS"><img src="https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/blt90a52ed02808cbc1/650c1dadb25642d90add1e4e/logo-nav-dropdown-48x48-comcast.svg" alt="" class=""/></div><div class="Spotlight_card-body___rb9E"><p>Comcast transforms customer 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based on similarity. As such, you can use kNN for relevance ranking in the context of <a href=\"/what-is/natural-language-processing\" target=\"_self\">natural language processing algorithms</a>, for similarity search and recommendation engines, or product recommendations. Note that kNN is useful when you have a relatively small dataset.</p>"}},{"@type":"Question","name":"Is kNN supervised or unsupervised machine learning? ","acceptedAnswer":{"@type":"Answer","text":"<p>kNN is <a href=\"/what-is/supervised-machine-learning\" target=\"_self\">supervised machine learning</a>. It is fed a set of data it stores and only processes the data when queried.</p>"}}]]}</script><section aria-labelledby="what-is-knn" data-component-theme="" class="jsx-3636536621 Hero_hero__iEZMX position-relative one-column hero"><div class=""><div class="Patterns_patterns__3u_in container z-0"><div class="Patterns_left__g_wcp"><img src="/static-res/images/hero/generic-d-light-left.svg" alt=""/></div><div class="Patterns_right__kW4Ma"><img src="/static-res/images/hero/generic-a-light-right.svg" alt=""/></div></div></div><div class=""><div class="Hero_heroContent__S5fut justify-content-between"><div class="container"><div class="layout layout-05"></div><div class="row"><div class="col-12 col-sm-8 offset-sm-2"><div class="Hero_content__VSxmz align-self-center"><div class="jsx-1955866259 title-wrapper"><h1 tag="H1" id="what-is-knn" class="jsx-1955866259 h2">What is kNN?</h1></div><div class="Hero_ctaList__efelZ mt-5"><div class="Hero_cta__zIL_f"><a class="button btn-primary btn-large" href="https://www.elastic.co/guide/en/elasticsearch/reference/master/knn-search.html"><span class="jsx-1596590093 btn-copy">Explore kNN search</span></a></div></div></div></div><div class="col-12 col-sm-10 offset-sm-1"></div></div><div class="layout layout-05"></div></div></div></div></section><section class="jsx-3241481256 container pt-8 faq-content"><div class="jsx-3241481256 side-nav d-none d-lg-block"><div class="jsx-3241481256 wrapper my-4 pb-6 pr-6"><nav class="jsx-3241481256"><ul class="jsx-3241481256"></ul></nav></div></div><div class="jsx-3241481256 content pb-10 paragraph-medium main-header"><section id="" class="jsx-3293015939"><section id="knearest-neighbor-definition" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="k-nearest-neighbor-definition" class="jsx-1955866259 h5">K-nearest neighbor definition</h3></div><p><strong>kNN, or the k-nearest neighbor algorithm, is a machine learning algorithm that uses proximity to compare one data point with a set of data it was trained on and has memorized to make predictions.</strong> This instance-based learning affords kNN the 'lazy learning' denomination and enables the algorithm to perform classification or regression problems. kNN works off the assumption that similar points can be found near one another — birds of a feather flock together.</p><p>As a classification algorithm, kNN assigns a new data point to the majority set within its neighbors. As a regression algorithm, kNN makes a prediction based on the average of the values closest to the query point.</p><p>kNN is a <a href="/what-is/supervised-machine-learning" target="_self">supervised learning algorithm</a> in which 'k' represents the number of nearest neighbors considered in the classification or regression problem, and 'NN' stands for the nearest neighbors to the number chosen for k.</p></div></section><section id="brief-history-of-the-knn-algorithm" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="brief-history-of-the-knn-algorithm" class="jsx-1955866259 h5">Brief history of the kNN algorithm </h3></div><p>kNN was first developed by Evelyn Fix and Joseph Hodges in 1951 in the context of research performed for the US military<sup>1</sup>. They published a paper explaining discriminant analysis, which is a non-parametric classification method. In 1967, Thomas Cover and Peter Hart expanded on the non-parametric classification method and published their "Nearest Neighbor Pattern Classification" paper<sup>2</sup>. Almost 20 years later, the algorithm was refined by James Keller, who developed a "fuzzy KNN" that produces lower error rates<sup>3</sup>.</p><p>Today, the kNN algorithm is the most widely used algorithm due to its adaptability to most fields — from genetics to finance and customer service.</p></div></section><section id="how-does-knn-work" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="how-does-knn-work" class="jsx-1955866259 h5">How does kNN work?</h3></div><p>The kNN algorithm works as <a href="/what-is/supervised-machine-learning" target="_self">a supervised learning algorithm</a>, meaning it is fed training datasets it memorizes. It relies on this labeled input data to learn a function that produces an appropriate output when given new unlabeled data.</p><p>This enables the algorithm to solve classification or regression problems. While kNN's computation occurs during a query and not during a training phase, it has important data storage requirements and is therefore heavily reliant on memory.</p><p>For classification problems, the KNN algorithm will assign a class label based on a majority, meaning that it will use the label that is most frequently present around a given data point. In other words, the output of a classification problem is the <strong>mode</strong> of the nearest neighbors.</p><blockquote><p><strong>A distinction: majority voting vs. plurality voting</strong></p><p>Majority voting denotes anything over 50% as the majority. This applies if there are two class labels in consideration. However, plurality voting applies if multiple class labels are being considered. In these cases, anything over 33.3% would be sufficient to denote a majority, and hence provide a prediction. Plurality voting is therefore the more accurate term to define kNN's mode.</p><p>If we were to illustrate this distinction:</p><p>A binary prediction</p><p>Y: 🎉🎉🎉❤️❤️❤️❤️❤️</p><p>Majority vote: ❤️</p><p>Plurality vote: ❤️</p><p>A multi-class setting</p><p>Y: ⏰⏰⏰💰💰💰🏠🏠🏠🏠</p><p>Majority vote: None</p><p>Plurality vote: 🏠</p></blockquote><p>Regression problems use the <strong>mean</strong> of the nearest neighbors to predict a classification. A regression problem will produce real numbers as the query output.</p><p>For example, if you were making a chart to predict someone's weight based on their height, the values denoting height would be independent, while the values for weight would be dependent. By performing a calculation of the average height-to-weight ratio, you could estimate someone's weight (the dependent variable) based on their height (the independent variable).</p></div></section><section id="4-types-of-computing-knn-distance-metrics" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="4-types-of-computing-knn-distance-metrics" class="jsx-1955866259 h5">4 types of computing kNN distance metrics</h3></div><p>The key to the kNN algorithm is determining the distance between the query point and the other data points. Determining distance metrics enables decision boundaries. These boundaries create different data point regions. There are different methods used to calculate distance:</p><ul class="list-green"><li><strong>Euclidean distance</strong> is the most common distance measure, which measures a straight line between the query point and the other point being measured.</li><li><strong>Manhattan distance</strong> is also a popular distance measure, which measures the absolute value between two points. It is represented on a grid, and often referred to as taxicab geometry — how do you travel from point A (your query point) to point B (the point being measured)?</li><li><strong>Minkowski distance</strong> is a generalization of Euclidean and Manhattan distance metrics, which enables the creation of other distance metrics. It is calculated in a normed vector space. In the Minkowski distance, p is the parameter that defines the type of distance used in the calculation. If p=1, then the Manhattan distance is used. If p=2, then the Euclidean distance is used.</li><li><strong>Hamming distance</strong>, also referred to as the overlap metric, is a technique used with Boolean or string vectors to identify where vectors do not match. In other words, it measures the distance between two strings of equal length. It is especially useful for error detection and error correction codes.</li></ul><p><img asset_uid="bltf21aa7ceeb3a8512" src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltf21aa7ceeb3a8512/66cf7e13651b1469ce2973b4/vector-database-architecture-infographic-2-vector-search.jpg" alt="Diagram of how a vector search engine works using vector embeddings" height="auto"/></p></div></section><section id="how-to-choose-the-best-k-value" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="how-to-choose-the-best-k-value" class="jsx-1955866259 h5">How to choose the best k value </h3></div><p>To choose the best k value — the number of nearest neighbors considered — you must experiment with a few values to find the k value that generates the most accurate predictions with the fewest number of errors. Determining the best value is a balancing act:</p><ul class="list-green"><li><strong>Low k values make predictions unstable.</strong><br/>Take this example: a query point is surrounded by 2 green dots and one red triangle. If k=1 and it happens that the point closest to the query point is one of the green dots, the algorithm will incorrectly predict a green dot as the outcome of the query. Low k values are high variance (the model fits too closely to the training data), high complexity, and low bias (the model is complex enough to fit the training data well).</li><li><strong>High k values are noisy.</strong><br/>A higher k value will increase the accuracy of predictions because there are more numbers of which to calculate the modes or means. However, if the k value is too high, it will likely result in low variance, low complexity, and high bias (the model is NOT complex enough to fit the training data well).</li></ul><p>Ideally, you want to find a k value that is between high variance and high bias. It is also recommended to choose an odd number for k to avoid ties in classification analysis.</p><p>The right k value is also relative to your data set. To choose that value, you might try to find the square root of N, where N is the number of data points in the training dataset. Cross-validation tactics can also help you choose the k value best suited to your dataset.</p></div></section><section id="advantages-of-the-knn-algorithm" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="advantages-of-the-knn-algorithm" class="jsx-1955866259 h5">Advantages of the kNN algorithm</h3></div><p>The kNN algorithm is often described as the “simplest” supervised learning algorithm, which leads to its several advantages:</p><ul class="list-green"><li><strong>Simple:</strong> kNN is easy to implement because of how simple and accurate it is. As such, it is often one of the first classifiers that a data scientist will learn.</li><li><strong>Adaptable:</strong> As soon as new training samples are added to its dataset, the kNN algorithm adjusts its predictions to include the new training data.</li><li><strong>Easily programmable:</strong> kNN requires only a few hyperparameters — a k value and a distance metric. This makes it a fairly uncomplicated algorithm.</li></ul><p>In addition, the kNN algorithm requires no training time because it stores training data and its computational power is used only when making predictions.</p></div></section><section id="challenges-and-limitations-of-knn" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="challenges-and-limitations-of-knn" class="jsx-1955866259 h5">Challenges and limitations of kNN </h3></div><p>While the kNN algorithm is simple, it also has a set of challenges and limitations, due in part to its simplicity:</p><ul class="list-green"><li><strong>Difficult to scale:</strong> Because kNN takes up a lot of memory and data storage, it brings up the expenses associated with storage. This reliance on memory also means that the algorithm is computationally intensive, which is in turn resource-intensive.</li><li><strong>Curse of dimensionality:</strong> This refers to a phenomenon that occurs in computer science, wherein a fixed set of training examples is challenged by an increasing number of dimensions and the inherent increase of feature values in these dimensions. In other words, the model’s training data can’t keep up with the evolving dimensionality of the hyperspace. This means that predictions become less accurate because the distance between the query point and similar points grows wider — on other dimensions.</li><li><strong>Overfitting:</strong> The value of k, as shown earlier, will impact the algorithm’s behavior. This can happen especially when the value of k is too low. Lower values of k can overfit the data, while higher values of k will ‘smooth’ the prediction values because the algorithm averages values over a greater area.</li></ul></div></section><section id="top-knn-use-cases" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="top-knn-use-cases" class="jsx-1955866259 h5">Top kNN use cases</h3></div><p>The kNN algorithm, popular for its simplicity and accuracy, has a variety of applications, especially when used for classification analysis.</p><ul class="list-green"><li><strong>Relevance ranking:</strong> kNN uses <a href="/what-is/natural-language-processing" target="_self">natural language processing (NLP)</a> algorithms to determine which results are most relevant to a query.</li><li><strong>Similarity search for images or videos:</strong> Image similarity search uses natural language descriptions to find images matching from text queries.</li></ul><p><img asset_uid="blt10700ec980cdade3" src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt10700ec980cdade3/63fe208f127b1e1130912ccb/blog-elastic-step-3-result-matching-images.png" alt="blog-elastic-step-3-result-matching-images.png" height="auto"/></p><ul class="list-green"><li><strong>Pattern recognition:</strong> kNN can be used to identify patterns in text or digit classification.</li><li><strong>Finance:</strong> In the financial sector, kNN can be used for stock market forecasting, currency exchange rates, etc.</li><li><strong>Product recommendations and recommendation engines:</strong> Think Netflix! "If you liked this, we think you'll also like…" Any site that uses a version of that sentence, overtly or not, is likely using a kNN algorithm to power its recommendation engine.</li><li><strong>Healthcare:</strong> In the field of medicine and medical research, the kNN algorithm can be used in genetics to calculate the probability of certain gene expressions. This allows doctors to predict the likelihood of cancer, heart attacks, or any other hereditary conditions.</li><li><strong>Data preprocessing:</strong> The kNN algorithm can be used to estimate missing values in datasets.</li></ul></div></section><section id="knn-search-with-elastic" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="knn-search-with-elastic" class="jsx-1955866259 h5">kNN search with Elastic</h3></div><p><a href="/elasticsearch" target="_self">Elasticsearch</a> enables you to implement kNN search. Two methods are supported: approximate kNN and exact, brute-force kNN. You can use kNN search in the context of similarity search, relevance ranking based on NLP algorithms, and product recommendations and recommendation engines.</p><p><a href="https://www.elastic.co/guide/en/elasticsearch/reference/master/knn-search.html" target="_self">Implement kNN search with Elastic</a></p></div></section><section id="knn-resources" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="knn-resources" class="jsx-1955866259 h5">kNN resources </h3></div><ul class="list-green"><li><a href="https://www.elastic.co/guide/en/elasticsearch/reference/master/knn-search.html">Take a deeper dive into kNN search</a></li><li><a href="/blog/ann-vs-knn">aNN vs kNN</a></li><li><a href="/blog/understanding-ann">Understanding the approximate nearest neighbor algorithm</a></li><li><a href="/blog/understanding-ai-search-algorithms">Understanding AI search algorithms</a></li></ul></div></section><hr class="jsx-3293015939 mt-6 mb-0"/></section><section id="knearest-neighbors-faq" class="jsx-3293015939"><div class="jsx-1955866259 title-wrapper"><h2 tag="H2" id="k-nearest-neighbors-faq" class="jsx-1955866259 h4">K-nearest neighbors FAQ</h2></div><section id="when-to-use-knn" class="jsx-1043069612"><div itemscope="" itemProp="mainEntity" itemType="https://schema.org/Question" class="jsx-1043069612"><div itemProp="name" class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="when-to-use-knn" class="jsx-1955866259 h5">When to use kNN?</h3></div></div><div itemscope="" itemProp="acceptedAnswer" itemType="https://schema.org/Answer" class="jsx-1043069612"><div itemProp="text" class="jsx-1043069612"><p>Use kNN to make predictions based on similarity. As such, you can use kNN for relevance ranking in the context of <a href="/what-is/natural-language-processing" target="_self">natural language processing algorithms</a>, for similarity search and recommendation engines, or product recommendations. Note that kNN is useful when you have a relatively small dataset.</p></div></div></div></section><section id="is-knn-supervised-or-unsupervised-machine-learning" class="jsx-1043069612"><div itemscope="" itemProp="mainEntity" itemType="https://schema.org/Question" class="jsx-1043069612"><div itemProp="name" class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="is-knn-supervised-or-unsupervised-machine-learning" class="jsx-1955866259 h5">Is kNN supervised or unsupervised machine learning? </h3></div></div><div itemscope="" itemProp="acceptedAnswer" itemType="https://schema.org/Answer" class="jsx-1043069612"><div itemProp="text" class="jsx-1043069612"><p>kNN is <a href="/what-is/supervised-machine-learning" target="_self">supervised machine learning</a>. It is fed a set of data it stores and only processes the data when queried.</p></div></div></div></section><section id="what-does-knn-stand-for" class="jsx-1043069612"><div class="jsx-1043069612"><div class="jsx-1955866259 title-wrapper"><h3 tag="H3" id="what-does-knn-stand-for" class="jsx-1955866259 h5">What does kNN stand for?</h3></div><p>kNN stands for k-nearest neighbor algorithm, where k denotes the number of nearest neighbors considered in the analysis.</p></div></section><hr class="jsx-3293015939 mt-6 mb-0"/></section><section id="footnotes" class="jsx-3293015939"><div class="jsx-1955866259 title-wrapper"><h2 tag="H2" id="footnotes" class="jsx-1955866259 h4">Footnotes</h2></div><ol><li>Silverman, B.W., & Jones, M.C. (1989). E. Fix and J.L Hodes (1951): An Important Contribution to Nonparametric Discriminant Analysis and Density Estimation: Commentary on Fix and Hodges (1951). International Statistical Institute (ISI) / Revue Internationale de Statistique,57(3), 233–238. <a href="https://doi.org/10.2307/1403796" target="_self">https://doi.org/10.2307/1403796</a></li><li>T. Cover and P. Hart, "Nearest neighbor pattern classification," in IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21-27, January 1967, doi: 10.1109/TIT.1967.1053964. <a href="https://ieeexplore.ieee.org/document/1053964/authors#authors" target="_self">https://ieeexplore.ieee.org/document/1053964/authors#authors</a></li><li>K-Nearest Neighbors Algorithm: Classification and Regression Star, History of Data Science, Accessed: 10/23/2023, <a href="https://www.historyofdatascience.com/k-nearest-neighbors-algorithm-classification-and-regression-star/" target="_self">https://www.historyofdatascience.com/k-nearest-neighbors-algorithm-classification-and-regression-star/</a></li></ol><hr class="jsx-3293015939 mt-6 mb-0"/></section><div class="layout layout-07"></div></div></section></div></main><div id="elastic-footer"></div><footer class="Footer_footer__Q5T55" aria-label="footer"><div class="Footer_footerWrapper__yhyD_ bg-dark-ink" data-component-theme="dark"><div class="container"><div 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As a regression algorithm, kNN makes a prediction based on the average of the values closest to the query point.\u003c/p\u003e\u003cp\u003ekNN is a \u003ca href=\"/what-is/supervised-machine-learning\" target=\"_self\"\u003esupervised learning algorithm\u003c/a\u003e in which 'k' represents the number of nearest neighbors considered in the classification or regression problem, and 'NN' stands for the nearest neighbors to the number chosen for k.\u003c/p\u003e","enable_structured_data":false},{"title_l10n":"Brief history of the kNN algorithm ","_metadata":{"uid":"cs9d3fb83bf43ff3b6"},"link":{"title":"Brief history of the kNN algorithm ","href":"brief-history-of-the-knn-algorithm"},"paragraph_l10n":"\u003cp\u003ekNN was first developed by Evelyn Fix and Joseph Hodges in 1951 in the context of research performed for the US military\u003csup\u003e1\u003c/sup\u003e. They published a paper explaining discriminant analysis, which is a non-parametric classification method. In 1967, Thomas Cover and Peter Hart expanded on the non-parametric classification method and published their \"Nearest Neighbor Pattern Classification\" paper\u003csup\u003e2\u003c/sup\u003e. Almost 20 years later, the algorithm was refined by James Keller, who developed a \"fuzzy KNN\" that produces lower error rates\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eToday, the kNN algorithm is the most widely used algorithm due to its adaptability to most fields — from genetics to finance and customer service.\u003c/p\u003e","enable_structured_data":false},{"title_l10n":"How does kNN work?","_metadata":{"uid":"cs19c746832ea23481"},"link":{"title":"How does kNN work?","href":"how-does-knn-work"},"paragraph_l10n":"\u003cp\u003eThe kNN algorithm works as \u003ca href=\"/what-is/supervised-machine-learning\" target=\"_self\"\u003ea supervised learning algorithm\u003c/a\u003e, meaning it is fed training datasets it memorizes. It relies on this labeled input data to learn a function that produces an appropriate output when given new unlabeled data.\u003c/p\u003e\u003cp\u003eThis enables the algorithm to solve classification or regression problems. While kNN's computation occurs during a query and not during a training phase, it has important data storage requirements and is therefore heavily reliant on memory.\u003c/p\u003e\u003cp\u003eFor classification problems, the KNN algorithm will assign a class label based on a majority, meaning that it will use the label that is most frequently present around a given data point. In other words, the output of a classification problem is the \u003cstrong\u003emode\u003c/strong\u003e of the nearest neighbors.\u003c/p\u003e\u003cblockquote\u003e\u003cp\u003e\u003cstrong\u003eA distinction: majority voting vs. plurality voting\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMajority voting denotes anything over 50% as the majority. This applies if there are two class labels in consideration. However, plurality voting applies if multiple class labels are being considered. In these cases, anything over 33.3% would be sufficient to denote a majority, and hence provide a prediction. Plurality voting is therefore the more accurate term to define kNN's mode.\u003c/p\u003e\u003cp\u003eIf we were to illustrate this distinction:\u003c/p\u003e\u003cp\u003eA binary prediction\u003c/p\u003e\u003cp\u003eY: 🎉🎉🎉❤️❤️❤️❤️❤️\u003c/p\u003e\u003cp\u003eMajority vote: ❤️\u003c/p\u003e\u003cp\u003ePlurality vote: ❤️\u003c/p\u003e\u003cp\u003eA multi-class setting\u003c/p\u003e\u003cp\u003eY: ⏰⏰⏰💰💰💰🏠🏠🏠🏠\u003c/p\u003e\u003cp\u003eMajority vote: None\u003c/p\u003e\u003cp\u003ePlurality vote: 🏠\u003c/p\u003e\u003c/blockquote\u003e\u003cp\u003eRegression problems use the \u003cstrong\u003emean\u003c/strong\u003e of the nearest neighbors to predict a classification. A regression problem will produce real numbers as the query output.\u003c/p\u003e\u003cp\u003eFor example, if you were making a chart to predict someone's weight based on their height, the values denoting height would be independent, while the values for weight would be dependent. By performing a calculation of the average height-to-weight ratio, you could estimate someone's weight (the dependent variable) based on their height (the independent variable).\u003c/p\u003e","enable_structured_data":false},{"title_l10n":"4 types of computing kNN distance metrics","_metadata":{"uid":"cse7c15983c7ef3a53"},"link":{"title":"4 types of computing kNN distance metrics","href":"4-types-of-computing-knn-distance-metrics"},"paragraph_l10n":"\u003cp\u003eThe key to the kNN algorithm is determining the distance between the query point and the other data points. Determining distance metrics enables decision boundaries. These boundaries create different data point regions. There are different methods used to calculate distance:\u003c/p\u003e\n\u003cul class=\"list-green\"\u003e\n \u003cli\u003e\u003cstrong\u003eEuclidean distance\u003c/strong\u003e is the most common distance measure, which measures a straight line between the query point and the other point being measured.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eManhattan distance\u003c/strong\u003e is also a popular distance measure, which measures the absolute value between two points. It is represented on a grid, and often referred to as taxicab geometry — how do you travel from point A (your query point) to point B (the point being measured)?\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMinkowski distance\u003c/strong\u003e is a generalization of Euclidean and Manhattan distance metrics, which enables the creation of other distance metrics. It is calculated in a normed vector space. In the Minkowski distance, p is the parameter that defines the type of distance used in the calculation. If p=1, then the Manhattan distance is used. If p=2, then the Euclidean distance is used.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHamming distance\u003c/strong\u003e, also referred to as the overlap metric, is a technique used with Boolean or string vectors to identify where vectors do not match. In other words, it measures the distance between two strings of equal length. It is especially useful for error detection and error correction codes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cimg asset_uid=\"bltf21aa7ceeb3a8512\" src=\"https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltf21aa7ceeb3a8512/66cf7e13651b1469ce2973b4/vector-database-architecture-infographic-2-vector-search.jpg\" alt=\"Diagram of how a vector search engine works using vector embeddings\" height=\"auto\" /\u003e\u003c/p\u003e","enable_structured_data":false},{"title_l10n":"How to choose the best k value ","_metadata":{"uid":"cs6c36cda0a7778f63"},"link":{"title":"How to choose the best k value ","href":"how-to-choose-the-best-k-value"},"paragraph_l10n":"\u003cp\u003eTo choose the best k value — the number of nearest neighbors considered — you must experiment with a few values to find the k value that generates the most accurate predictions with the fewest number of errors. Determining the best value is a balancing act:\u003c/p\u003e\n\u003cul class=\"list-green\"\u003e\n \u003cli\u003e\u003cstrong\u003eLow k values make predictions unstable.\u003c/strong\u003e\u003cbr /\u003eTake this example: a query point is surrounded by 2 green dots and one red triangle. If k=1 and it happens that the point closest to the query point is one of the green dots, the algorithm will incorrectly predict a green dot as the outcome of the query. Low k values are high variance (the model fits too closely to the training data), high complexity, and low bias (the model is complex enough to fit the training data well).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHigh k values are noisy.\u003c/strong\u003e\u003cbr /\u003eA higher k value will increase the accuracy of predictions because there are more numbers of which to calculate the modes or means. However, if the k value is too high, it will likely result in low variance, low complexity, and high bias (the model is NOT complex enough to fit the training data well).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIdeally, you want to find a k value that is between high variance and high bias. It is also recommended to choose an odd number for k to avoid ties in classification analysis.\u003c/p\u003e\n\u003cp\u003eThe right k value is also relative to your data set. To choose that value, you might try to find the square root of N, where N is the number of data points in the training dataset. Cross-validation tactics can also help you choose the k value best suited to your dataset.\u003c/p\u003e","enable_structured_data":false},{"title_l10n":"Advantages of the kNN algorithm","_metadata":{"uid":"csa9c70a307db91437"},"link":{"title":"Advantages of the kNN algorithm","href":"advantages-of-the-knn-algorithm"},"paragraph_l10n":"\u003cp\u003eThe kNN algorithm is often described as the “simplest” supervised learning algorithm, which leads to its several advantages:\u003c/p\u003e\n\u003cul class=\"list-green\"\u003e\n \u003cli\u003e\u003cstrong\u003eSimple:\u003c/strong\u003e kNN is easy to implement because of how simple and accurate it is. As such, it is often one of the first classifiers that a data scientist will learn.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAdaptable:\u003c/strong\u003e As soon as new training samples are added to its dataset, the kNN algorithm adjusts its predictions to include the new training data.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEasily programmable:\u003c/strong\u003e kNN requires only a few hyperparameters — a k value and a distance metric. This makes it a fairly uncomplicated algorithm.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition, the kNN algorithm requires no training time because it stores training data and its computational power is used only when making predictions.\u003c/p\u003e","enable_structured_data":false},{"title_l10n":"Challenges and limitations of kNN ","_metadata":{"uid":"cs5fd07e2a80f0260e"},"link":{"title":"Challenges and limitations of kNN ","href":"challenges-and-limitations-of-knn"},"paragraph_l10n":"\u003cp\u003eWhile the kNN algorithm is simple, it also has a set of challenges and limitations, due in part to its simplicity:\u003c/p\u003e\n\u003cul class=\"list-green\"\u003e\n \u003cli\u003e\u003cstrong\u003eDifficult to scale:\u003c/strong\u003e Because kNN takes up a lot of memory and data storage, it brings up the expenses associated with storage. This reliance on memory also means that the algorithm is computationally intensive, which is in turn resource-intensive.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCurse of dimensionality:\u003c/strong\u003e This refers to a phenomenon that occurs in computer science, wherein a fixed set of training examples is challenged by an increasing number of dimensions and the inherent increase of feature values in these dimensions. In other words, the model’s training data can’t keep up with the evolving dimensionality of the hyperspace. This means that predictions become less accurate because the distance between the query point and similar points grows wider — on other dimensions.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOverfitting:\u003c/strong\u003e The value of k, as shown earlier, will impact the algorithm’s behavior. This can happen especially when the value of k is too low. Lower values of k can overfit the data, while higher values of k will ‘smooth’ the prediction values because the algorithm averages values over a greater area.\u003c/li\u003e\n\u003c/ul\u003e","enable_structured_data":false},{"title_l10n":"Top kNN use cases","_metadata":{"uid":"cs15fae4fa5161a114"},"link":{"title":"Top kNN use cases","href":"top-knn-use-cases"},"paragraph_l10n":"\u003cp\u003eThe kNN algorithm, popular for its simplicity and accuracy, has a variety of applications, especially when used for classification analysis.\u003c/p\u003e\n\u003cul class=\"list-green\"\u003e\n \u003cli\u003e\u003cstrong\u003eRelevance ranking:\u003c/strong\u003e kNN uses \u003ca href=\"/what-is/natural-language-processing\" target=\"_self\"\u003enatural language processing (NLP)\u003c/a\u003e algorithms to determine which results are most relevant to a query.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSimilarity search for images or videos:\u003c/strong\u003e Image similarity search uses natural language descriptions to find images matching from text queries.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cimg asset_uid=\"blt10700ec980cdade3\" src=\"https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt10700ec980cdade3/63fe208f127b1e1130912ccb/blog-elastic-step-3-result-matching-images.png\" alt=\"blog-elastic-step-3-result-matching-images.png\" height=\"auto\" /\u003e\u003c/p\u003e\n\u003cul class=\"list-green\"\u003e\n \u003cli\u003e\u003cstrong\u003ePattern recognition:\u003c/strong\u003e kNN can be used to identify patterns in text or digit classification.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFinance:\u003c/strong\u003e In the financial sector, kNN can be used for stock market forecasting, currency exchange rates, etc.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eProduct recommendations and recommendation engines:\u003c/strong\u003e Think Netflix! \"If you liked this, we think you'll also like…\" Any site that uses a version of that sentence, overtly or not, is likely using a kNN algorithm to power its recommendation engine.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHealthcare:\u003c/strong\u003e In the field of medicine and medical research, the kNN algorithm can be used in genetics to calculate the probability of certain gene expressions. This allows doctors to predict the likelihood of cancer, heart attacks, or any other hereditary conditions.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eData preprocessing:\u003c/strong\u003e The kNN algorithm can be used to estimate missing values in datasets.\u003c/li\u003e\n\u003c/ul\u003e","enable_structured_data":false},{"title_l10n":"kNN search with Elastic","_metadata":{"uid":"cs70b6e3de8a04e82a"},"link":{"title":"kNN search with Elastic","href":"knn-search-with-elastic"},"paragraph_l10n":"\u003cp\u003e\u003ca href=\"/elasticsearch\" target=\"_self\"\u003eElasticsearch\u003c/a\u003e enables you to implement kNN search. Two methods are supported: approximate kNN and exact, brute-force kNN. You can use kNN search in the context of similarity search, relevance ranking based on NLP algorithms, and product recommendations and recommendation engines.\u003c/p\u003e\u003cp\u003e\u003ca href=\"https://www.elastic.co/guide/en/elasticsearch/reference/master/knn-search.html\" target=\"_self\"\u003eImplement kNN search with Elastic\u003c/a\u003e\u003c/p\u003e","enable_structured_data":false},{"title_l10n":"kNN resources ","link":{"title":"kNN resources ","href":"knn-resources"},"paragraph_l10n":"\u003cul class=\"list-green\"\u003e\u003cli\u003e\u003ca href=\"https://www.elastic.co/guide/en/elasticsearch/reference/master/knn-search.html\"\u003eTake a deeper dive into kNN search\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca href=\"/blog/ann-vs-knn\"\u003eaNN vs kNN\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca href=\"/blog/understanding-ann\"\u003eUnderstanding the approximate nearest neighbor algorithm\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca href=\"/blog/understanding-ai-search-algorithms\"\u003eUnderstanding AI search algorithms\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","enable_structured_data":false,"_metadata":{"uid":"cs3e4d9edc95e91c81"}}]},{"title_l10n":"K-nearest neighbors FAQ","_metadata":{"uid":"cs7c20717d3cb170b7"},"link":{"title":"K-nearest neighbors FAQ","href":"knearest-neighbors-faq"},"paragraph_l10n":"","enable_structured_data":false,"subtopic":[{"title_l10n":"When to use kNN?","_metadata":{"uid":"cs16d0874235690524"},"link":{"title":"When to use kNN?","href":"when-to-use-knn"},"paragraph_l10n":"\u003cp\u003eUse kNN to make predictions based on similarity. As such, you can use kNN for relevance ranking in the context of \u003ca href=\"/what-is/natural-language-processing\" target=\"_self\"\u003enatural language processing algorithms\u003c/a\u003e, for similarity search and recommendation engines, or product recommendations. Note that kNN is useful when you have a relatively small dataset.\u003c/p\u003e","enable_structured_data":true},{"title_l10n":"Is kNN supervised or unsupervised machine learning? ","_metadata":{"uid":"cs9ffcf864b8a73413"},"link":{"title":"Is kNN supervised or unsupervised machine learning? ","href":"is-knn-supervised-or-unsupervised-machine-learning"},"paragraph_l10n":"\u003cp\u003ekNN is \u003ca href=\"/what-is/supervised-machine-learning\" target=\"_self\"\u003esupervised machine learning\u003c/a\u003e. It is fed a set of data it stores and only processes the data when queried.\u003c/p\u003e","enable_structured_data":true},{"title_l10n":"What does kNN stand for?","_metadata":{"uid":"cs1827eff3743fc1f8"},"link":{"title":"What does kNN stand for?","href":"what-does-knn-stand-for"},"paragraph_l10n":"\u003cp\u003ekNN stands for k-nearest neighbor algorithm, where k denotes the number of nearest neighbors considered in the analysis.\u003c/p\u003e","enable_structured_data":false}]},{"title_l10n":"Footnotes","_metadata":{"uid":"cs0e6e27ef01fabad3"},"link":{"title":"Footnotes","href":"footnotes"},"paragraph_l10n":"\u003col\u003e\u003cli\u003eSilverman, B.W., \u0026amp; Jones, M.C. (1989). E. Fix and J.L Hodes (1951): An Important Contribution to Nonparametric Discriminant Analysis and Density Estimation: Commentary on Fix and Hodges (1951). International Statistical Institute (ISI) / Revue Internationale de Statistique,57(3), 233–238. \u003ca href=\"https://doi.org/10.2307/1403796\" target=\"_self\"\u003ehttps://doi.org/10.2307/1403796\u003c/a\u003e\u003c/li\u003e\u003cli\u003eT. Cover and P. Hart, \"Nearest neighbor pattern classification,\" in IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21-27, January 1967, doi: 10.1109/TIT.1967.1053964. \u003ca href=\"https://ieeexplore.ieee.org/document/1053964/authors#authors\" target=\"_self\"\u003ehttps://ieeexplore.ieee.org/document/1053964/authors#authors\u003c/a\u003e\u003c/li\u003e\u003cli\u003eK-Nearest Neighbors Algorithm: Classification and Regression Star, History of Data Science, Accessed: 10/23/2023, \u003ca href=\"https://www.historyofdatascience.com/k-nearest-neighbors-algorithm-classification-and-regression-star/\" target=\"_self\"\u003ehttps://www.historyofdatascience.com/k-nearest-neighbors-algorithm-classification-and-regression-star/\u003c/a\u003e\u003c/li\u003e\u003c/ol\u003e","enable_structured_data":false,"subtopic":[]}],"updated_at":"2024-11-06T18:23:01.735Z","updated_by":"blt3044324473ef223b70bc674c","url":"/what-is/knn","publish_details":{"time":"2024-11-06T18:23:07.460Z","user":"blt3044324473ef223b70bc674c","environment":"blt835e2c3b7e9eb7fb","locale":"en-us"}},"locale":"","entryLocale":"en","isEntryLocalized":false,"translateContentRedesign":[{"uid":"bltfb44eb6e899331a4","_version":1,"locale":"en-us","ACL":{},"created_at":"2024-11-13T23:43:06.196Z","created_by":"blt36e890d06c5ec32c","english_content":"Or","tags":[],"title":"Or","translate_content_l10n":"Or","updated_at":"2024-11-13T23:43:06.196Z","updated_by":"blt36e890d06c5ec32c","publish_details":{"time":"2024-11-13T23:44:21.198Z","user":"blt36e890d06c5ec32c","environment":"blt835e2c3b7e9eb7fb","locale":"en-us"}},{"uid":"blt2aeecde6c1729945","_version":4,"locale":"en-us","ACL":{},"created_at":"2023-01-26T21:09:02.610Z","created_by":"blt36e890d06c5ec32c","english_content":"Load more","tags":[],"title":"Load more","translate_content_l10n":"Load more","updated_at":"2024-09-03T16:41:46.744Z","updated_by":"blt36e890d06c5ec32c","publish_details":{"time":"2024-09-03T16:42:00.289Z","user":"blt36e890d06c5ec32c","environment":"blt835e2c3b7e9eb7fb","locale":"en-us"}},{"_version":1,"locale":"en-us","uid":"blt6f3ad4a078adc611","ACL":{},"created_at":"2024-05-28T12:47:25.539Z","created_by":"blt3e52848e0cb3c394","english_content":"Overview","tags":[],"title":"Overview","translate_content_l10n":"Overview","updated_at":"2024-05-28T12:47:25.539Z","updated_by":"blt3e52848e0cb3c394","publish_details":{"environment":"blt835e2c3b7e9eb7fb","locale":"en-us","time":"2024-05-28T12:47:29.022Z","user":"blt3e52848e0cb3c394"}},{"_version":4,"locale":"en-us","uid":"blt3b08c089ffd331e6","ACL":{},"created_at":"2023-01-26T21:09:09.267Z","created_by":"blt36e890d06c5ec32c","english_content":"Can't make it? 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You'll also receive an email with related content.","updated_at":"2023-12-18T21:59:16.399Z","updated_by":"blt3044324473ef223b70bc674c","publish_details":{"environment":"blt835e2c3b7e9eb7fb","locale":"en-us","time":"2023-12-18T21:59:19.133Z","user":"blt3044324473ef223b70bc674c"}},{"_version":4,"locale":"en-us","uid":"blt7a2b7e50bb030ed8","ACL":{},"created_at":"2023-01-26T21:09:00.746Z","created_by":"blt36e890d06c5ec32c","english_content":"You'll also receive an email with related content.","tags":[],"title":"You'll also receive an email with related content","translate_content_l10n":"You'll also receive an email with related content.","updated_at":"2023-12-18T21:58:39.250Z","updated_by":"blt3044324473ef223b70bc674c","publish_details":{"environment":"blt835e2c3b7e9eb7fb","locale":"en-us","time":"2023-12-18T21:58:43.929Z","user":"blt3044324473ef223b70bc674c"}},{"_version":1,"locale":"en-us","uid":"blt7a2ecc87e95e0fed","ACL":{},"created_at":"2023-08-23T22:51:28.540Z","created_by":"blt36e890d06c5ec32c","english_content":"\u003cp\u003eBy submitting you acknowledge that you've read and agree to our \u003ca href=\"/legal/elastic-cloud-account-terms\" target=\"_blank\"\u003eTerms of Service\u003c/a\u003e, and that Elastic may \u003ca href=\"/legal/privacy-statement#how-we-use-the-information\" target=\"_blank\"\u003econtact you\u003c/a\u003e about our related products and services, using the details you provide above. 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