{"id":136630,"date":"2021-12-28T11:49:46","date_gmt":"2021-12-28T10:49:46","guid":{"rendered":"https:\/\/multi.datascientest.com\/?p=136630"},"modified":"2023-10-30T17:18:02","modified_gmt":"2023-10-30T16:18:02","slug":"que-es-el-algoritmo-knn","status":"publish","type":"post","link":"https:\/\/datascientest.com\/es\/que-es-el-algoritmo-knn","title":{"rendered":"\u00bfQu\u00e9 es el algoritmo KNN?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"136630\" class=\"elementor elementor-136630\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-09d569c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"09d569c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1891951\" data-id=\"1891951\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1aad1fe elementor-widget elementor-widget-text-editor\" data-id=\"1aad1fe\" 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<h2><b>El algoritmo de las K vecinas m\u00e1s cercanas o K-nearest neighbors (kNN) es un algoritmo de Machine Learning que pertenece a los algoritmos de aprendizaje supervisado simples y f\u00e1ciles de aplicar que pueden ser utilizados para resolver problemas de clasificaci\u00f3n y de regresi\u00f3n. En este art\u00edculo veremos la definici\u00f3n de este algoritmo, su funcionamiento as\u00ed como tambi\u00e9n una aplicaci\u00f3n directa en programaci\u00f3n.<\/b><\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9323e63 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9323e63\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f6287e7\" data-id=\"f6287e7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ae8aba5 elementor-widget elementor-widget-heading\" data-id=\"ae8aba5\" 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\">Definicion de KNN<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d746c65 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d746c65\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ed07e6d\" data-id=\"ed07e6d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-95fc6c3 elementor-widget elementor-widget-text-editor\" data-id=\"95fc6c3\" 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;\">Antes de concentrarnos en el <\/span><b>algoritmo KNN<\/b><span style=\"font-weight: 400;\">, es necesario retomar las bases. \u00bfQu\u00e9 es un algoritmo de aprendizaje supervisado?<\/span><\/p>\n<p><span style=\"font-weight: 400;\">En aprendizaje supervisado, un algoritmo recibe un conjunto de datos que est\u00e1n etiquetados con los valores de salida correspondientes sobre los que puede entrenarse y definir un modelo de predicci\u00f3n. Este algoritmo podr\u00e1 luego ser utilizado sobre datos nuevos para predecir sus valores de salida correspondientes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A continuaci\u00f3n, una ilustraci\u00f3n simplificada :<\/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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6620e31 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6620e31\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-973c216\" data-id=\"973c216\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1133ed3 elementor-widget elementor-widget-image\" data-id=\"1133ed3\" 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=\"800\" height=\"546\" src=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-1-KNN-1024x699.jpg\" class=\"attachment-large size-large wp-image-19106\" alt=\"knn\" srcset=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-1-KNN-1024x699.jpg 1024w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-1-KNN-300x205.jpg 300w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-1-KNN-768x524.jpg 768w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-1-KNN-1536x1049.jpg 1536w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-1-KNN-2048x1398.jpg 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d2c8af7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d2c8af7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ee6ff33\" data-id=\"ee6ff33\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d8a0ca8 elementor-widget elementor-widget-text-editor\" data-id=\"d8a0ca8\" 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;\">La l\u00f3gica detr\u00e1s del algoritmo de las K vecinas m\u00e1s cercanas es una de las m\u00e1s sencillas de todos los algoritmos de Machine Learning supervisados:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Etapa 1:<\/strong> Seleccionar el n\u00famero de K vecinas<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Etapa 2:<\/strong> Calcular la distancia<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b0acea0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b0acea0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-444f900\" data-id=\"444f900\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5b0e2ff elementor-widget elementor-widget-image\" data-id=\"5b0e2ff\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"363\" height=\"179\" src=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/eucledienne.png\" class=\"attachment-large size-large wp-image-19109\" alt=\"eucledienne\" srcset=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/eucledienne.png 363w, https:\/\/datascientest.com\/es\/files\/2020\/11\/eucledienne-300x148.png 300w\" sizes=\"(max-width: 363px) 100vw, 363px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">  Euclidienne  <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-94ded73\" data-id=\"94ded73\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ed02a88 elementor-widget elementor-widget-image\" data-id=\"ed02a88\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"471\" height=\"179\" src=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/m.png\" class=\"attachment-large size-large wp-image-19114\" alt=\"Manhattan\" srcset=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/m.png 471w, https:\/\/datascientest.com\/es\/files\/2020\/11\/m-300x114.png 300w\" sizes=\"(max-width: 471px) 100vw, 471px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Manhattan  <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fc00224 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fc00224\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5cb049f\" data-id=\"5cb049f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0b10fdc elementor-widget elementor-widget-text-editor\" data-id=\"0b10fdc\" 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;\">Desde un punto no clasificado a otros puntos:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Etapa 3:<\/strong> tomar las K vecinas m\u00e1s cercanas seg\u00fan la distancia calculada<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Etapa 4:<\/strong> entre las K vecinas, contar el n\u00famero de puntos en cada categor\u00eda.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Etapa 5:<\/strong> atribuir un nuevo punto a la categor\u00eda m\u00e1s presente entre las K vecinas<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Etapa 6:<\/strong> El modelo est\u00e1 listo.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fbe1258 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fbe1258\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9e88e33\" data-id=\"9e88e33\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c05bc39 elementor-widget elementor-widget-image\" data-id=\"c05bc39\" 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=\"384\" src=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-2-KNN-1024x492.jpg\" class=\"attachment-large size-large wp-image-19131\" alt=\"knn\" srcset=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-2-KNN-1024x492.jpg 1024w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-2-KNN-300x144.jpg 300w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-2-KNN-768x369.jpg 768w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-2-KNN-1536x737.jpg 1536w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-2-KNN-2048x983.jpg 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-60d4218 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"60d4218\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-447b7e7\" data-id=\"447b7e7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6289e63 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"6289e63\" 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-sm\" href=\"https:\/\/datascientest.com\/es\/formacion-data-scientist\">\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\">Aprender sobre el algoritmo KNN<\/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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8371a14 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8371a14\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-516aaaa\" data-id=\"516aaaa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-584bca8 elementor-widget elementor-widget-heading\" data-id=\"584bca8\" 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\">KNN : un ejemplo sobre su uso.<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7cc1740 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7cc1740\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ffa25c3\" data-id=\"ffa25c3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a81e7e9 elementor-widget elementor-widget-text-editor\" data-id=\"a81e7e9\" 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>A partir de ahora podemos analizar un ejemplo sobre el uso del algoritmo de las K vecinas. Gracias a la biblioteca <strong>Scikit-Learn<\/strong>, podemos importar la funci\u00f3n Neighbors Classifier que utilizaremos sobre un conjunto de datos IRIS.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7fb3781 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7fb3781\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-daf8fc0\" data-id=\"daf8fc0\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-54b5391 elementor-widget elementor-widget-image\" data-id=\"54b5391\" 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=\"244\" src=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-3-KNN-1024x312.jpg\" class=\"attachment-large size-large wp-image-19160\" alt=\"Knn\" srcset=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-3-KNN-1024x312.jpg 1024w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-3-KNN-300x92.jpg 300w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-3-KNN-768x234.jpg 768w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-3-KNN-1536x469.jpg 1536w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-3-KNN-2048x625.jpg 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0fadf90 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0fadf90\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0e9c028\" data-id=\"0e9c028\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3f26463 elementor-widget elementor-widget-text-editor\" data-id=\"3f26463\" 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;\">En el ejemplo, gracias al algoritmo KNN, obtenemos una excelente tasa de clasificaci\u00f3n correcta de las plantas cercana al 100%. Podemos tambi\u00e9n considerar una forma de elegir la<strong> K que hace que la clasificaci\u00f3n sea a\u00fan mejor<\/strong>. Una forma de encontrarla consiste en trazar el gr\u00e1fico del valor K y la tasa de error correspondiente al conjunto de datos :<\/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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9b64fe8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9b64fe8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1ad4bb5\" data-id=\"1ad4bb5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-870932d elementor-widget elementor-widget-image\" data-id=\"870932d\" 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=\"446\" src=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-4-KNN-1024x571.jpg\" class=\"attachment-large size-large wp-image-19161\" alt=\"knn\" srcset=\"https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-4-KNN-1024x571.jpg 1024w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-4-KNN-300x167.jpg 300w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-4-KNN-768x428.jpg 768w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-4-KNN-1536x856.jpg 1536w, https:\/\/datascientest.com\/es\/files\/2020\/11\/Illu-4-KNN-2048x1142.jpg 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d4f6dcf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d4f6dcf\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b38acfa\" data-id=\"b38acfa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f2c49f5 elementor-widget elementor-widget-text-editor\" data-id=\"f2c49f5\" 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;\">As\u00ed, podemos ver que la mejor tasa de predicci\u00f3n se obtiene con una K entre 5 y 18. Por encima de este valor, podemos observar un fen\u00f3meno llamado \u201coverlifting\u201d, o sobre aprendizaje en espanol, que se produce cuando los datos de aprendizaje utilizados para construir el modelo explican demasiado bien los datos pero no logran hacer predicciones \u00fatiles para nuevos datos.<\/span><\/p><p><span style=\"font-weight: 400;\">Algunos usos :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Puede utilizarse en <\/span><b>tecnolog\u00edas como OCR<\/b><span style=\"font-weight: 400;\"> (Optical Character Recognizer), que intenta detectar la escritura manuscrita, las im\u00e1genes e incluso los videos.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Puede utilizarse en el campo de las <\/span><b>notaciones de cr\u00e9dito<\/b><span style=\"font-weight: 400;\">. En ese caso, intenta hacer corresponder las caracter\u00edsticas de un individuo con el grupo de personas existentes para atribuirle la cotizaci\u00f3n de cr\u00e9dito correspondiente. Obtendr\u00e1 entonces con <\/span><b>la misma nota que las personas que corresponden a las mismas caracter\u00edsticas.<\/b><\/li><\/ul><p><span style=\"font-weight: 400;\">Se utiliza tambi\u00e9n para <\/span><b>predecir si el banco debe otorgar un pr\u00e9stamo a un particular.<\/b><span style=\"font-weight: 400;\"> Busca evaluar si el individuo dado corresponde a los mismos criterios que otras personas que han sido rechazadas anteriormente y sino, no es rechazado para el pr\u00e9stamo.<\/span><\/p><p><span style=\"font-weight: 400;\">Ventajas :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">El algoritmo es simple y f\u00e1cil de aplicar.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">No es necesario crear un modelo, configurar varios par\u00e1metros o formular hip\u00f3tesis suplementarias.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">El algoritmo es polivalente. Puede ser utilizado para la clasificaci\u00f3n o la regresi\u00f3n.<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Desventajas :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">El algoritmo se vuelve m\u00e1s lento a medida que el n\u00famero de observaciones aumenta y las variables independientes aumentan.<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Al tratarse de uno de los algoritmos m\u00e1s simples de Machine Learning, es muy implementado por los desarrolladores de sistemas basados en el aprendizaje, intuitivos e inteligentes que pueden efectuar y tomar peque\u00f1as decisiones solos.<\/span><\/p><p><span style=\"font-weight: 400;\">Esto hace que sea a\u00fan m\u00e1s pr\u00e1ctico para el aprendizaje y el desarrollo y puede servir para toda industria que utilice sistemas, soluciones o servicios inteligentes.<\/span><\/p><p><span style=\"font-weight: 400;\">Existen muchos otros algoritmos de <\/span><b>Clustering <\/b><span style=\"font-weight: 400;\">supervisado o no Supervisado que son m\u00e1s o menos utilizados seg\u00fan la situaci\u00f3n, como el de<\/span><b> K-means,<\/b><span style=\"font-weight: 400;\"> el de <\/span><b>CAH <\/b><span style=\"font-weight: 400;\">(Clasificaci\u00f3n ascendente jer\u00e1rquica), <\/span><b>DBSCAN <\/b><span style=\"font-weight: 400;\">(density-based spatial clustering of applications with noise), y muchos m\u00e1s que puedes descubrir en nuestro <\/span><b>blog<\/b><span style=\"font-weight: 400;\">!<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-24dfb53 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"24dfb53\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4318523\" data-id=\"4318523\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-200a1a7 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"200a1a7\" 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-sm\" href=\"https:\/\/datascientest.com\/es\/nuestros-cursos-de-data\">\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\">Descubre nuestras formaciones<\/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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>El algoritmo de las K vecinas m\u00e1s cercanas o K-nearest neighbors (kNN) es un algoritmo de Machine Learning que pertenece a los algoritmos de aprendizaje supervisado simples y f\u00e1ciles de aplicar que pueden ser utilizados para resolver problemas de clasificaci\u00f3n y de regresi\u00f3n. En este art\u00edculo veremos la definici\u00f3n de este algoritmo, su funcionamiento as\u00ed [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":19657,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427],"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>Descubra el algoritmo KNN : un algoritmo de aprendizaje supervisado<\/title>\n<meta name=\"description\" content=\"Descubra el algoritmo KNN : un algoritmo de aprendizaje supervisado muy popular en Clustering, una disciplina esencial en Machine Learning.\" \/>\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\/es\/que-es-el-algoritmo-knn\" 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