--- title: Understanding k-Nearest Neighbors | daily.dev description: kNN is a lazy machine learning algorithm that doesn't require training. It makes predictions by calculating distances between new samples and training samples,... canonical: https://daily.dev/posts/understanding-k-nearest-neighbors-yyeet6myj twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Understanding k-Nearest Neighbors | daily.dev og:description: kNN is a lazy machine learning algorithm that doesn't require training. It makes predictions by calculating distances between new samples and training samples,... og:url: https://daily.dev/posts/understanding-k-nearest-neighbors-yyeet6myj og:image: https://api.daily.dev/og/posts/YyeeT6MYJ.png og:image:alt: Understanding k-Nearest Neighbors og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Understanding k-Nearest Neighbors **[The Palindrome](https://daily.dev/sources/palindrome)** · 8 min read · 17 upvotes · 0 comments ## Summary kNN is a lazy machine learning algorithm that doesn't require training. It makes predictions by calculating distances between new samples and training samples, finding the k nearest neighbors, and classifying the new sample based on a majority vote from those neighbors. The value of 'k' is important as it determines the number of samples considered for prediction. The choice of 'k' can have a significant impact on the outcome. Different distance metrics, such as Euclidean distance, Manhattan distance, and Minkowski distance, can be used in kNN depending on the problem. Cosine similarity is another distance metric that can be useful for high-dimensional data. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#deep-learning](https://daily.dev/tags/deep-learning), [#classification](https://daily.dev/tags/classification) [View this post on daily.dev](https://daily.dev/posts/understanding-k-nearest-neighbors-yyeet6myj) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Understanding k-Nearest Neighbors","url":"https://daily.dev/posts/understanding-k-nearest-neighbors-yyeet6myj","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/understanding-k-nearest-neighbors-yyeet6myj"},"datePublished":"2024-05-09T19:40:38.174Z","dateModified":"2024-05-09T19:40:36.223Z","description":"kNN is a lazy machine learning algorithm that doesn't require training. It makes predictions by calculating distances between new samples and training samples,...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/224557b402aa2d1b0aae25b0f9804ca3?_a=AQAEuiZ","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/224557b402aa2d1b0aae25b0f9804ca3?_a=AQAEuiZ","isAccessibleForFree":true,"articleSection":"The Palindrome","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"The Palindrome","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/cfdfeb66e965438eb7a539c98ba83494","url":"https://daily.dev/sources/palindrome"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/understanding-k-nearest-neighbors-yyeet6myj","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":17},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,deep-learning,classification","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"The Palindrome","item":"https://daily.dev/sources/palindrome"},{"@type":"ListItem","position":3,"name":"Understanding k-Nearest Neighbors"}]} ```