{"id":1718,"date":"2023-07-25T06:28:26","date_gmt":"2023-07-25T06:28:26","guid":{"rendered":"https:\/\/statorials.org\/pl\/jak-interpretowac-mape\/"},"modified":"2023-07-25T06:28:26","modified_gmt":"2023-07-25T06:28:26","slug":"jak-interpretowac-mape","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/jak-interpretowac-mape\/","title":{"rendered":"Jak interpretowa\u0107 warto\u015bci mape"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Jedn\u0105 z najcz\u0119\u015bciej u\u017cywanych metryk do pomiaru dok\u0142adno\u015bci prognozy modelu jest <strong>\u015bredni bezwzgl\u0119dny b\u0142\u0105d procentowy<\/strong> , cz\u0119sto w skr\u00f3cie <strong>MAPE<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Oblicza si\u0119 go w nast\u0119puj\u0105cy spos\u00f3b:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>MAPE<\/strong> = (1\/n) * \u03a3(|rzeczywista \u2013 prognoza| \/ |rzeczywista|) * 100<\/span><\/p>\n<p> <span style=\"color: #000000;\">Z\u0142oto:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> \u2013 symbol oznaczaj\u0105cy \u201esum\u0119\u201d<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>n<\/strong> \u2013 wielko\u015b\u0107 pr\u00f3bki<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>current<\/strong> \u2013 Rzeczywista warto\u015b\u0107 danych<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>prognoza<\/strong> \u2013 przewidywana warto\u015b\u0107 danych<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">MAPE jest powszechnie u\u017cywany, poniewa\u017c jest \u0142atwy w interpretacji. Na<\/span> <span style=\"color: #000000;\">przyk\u0142ad warto\u015b\u0107 MAPE wynosz\u0105ca 14% oznacza, \u017ce \u015brednia r\u00f3\u017cnica mi\u0119dzy warto\u015bci\u0105 przewidywan\u0105 a warto\u015bci\u0105 rzeczywist\u0105 wynosi 14%.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Poni\u017cszy przyk\u0142ad pokazuje, jak obliczy\u0107 i zinterpretowa\u0107 warto\u015b\u0107 MAPE dla danego modelu.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Przyk\u0142ad: zinterpretuj warto\u015b\u0107 MAPE dla danego modelu<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Za\u0142\u00f3\u017cmy, \u017ce sie\u0107 spo\u017cywcza buduje model prognozowania przysz\u0142ej sprzeda\u017cy. Poni\u017cszy wykres przedstawia rzeczywist\u0105 sprzeda\u017c modelu oraz prognoz\u0119 sprzeda\u017cy na 12 kolejnych okres\u00f3w sprzeda\u017cowych:<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16913 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/mape_interpret1.png\" alt=\"\" width=\"215\" height=\"347\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">Do obliczenia bezwzgl\u0119dnego b\u0142\u0119du procentowego ka\u017cdej prognozy mo\u017cemy u\u017cy\u0107 nast\u0119puj\u0105cego wzoru:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Procent b\u0142\u0119du bezwzgl\u0119dnego = |rzeczywista prognoza| \/ |prawdziwy| *100<\/span> <\/li>\n<\/ul>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16914 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/mape_interpret2.png\" alt=\"\" width=\"353\" height=\"329\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">Nast\u0119pnie mo\u017cemy obliczy\u0107 \u015bredni\u0105 warto\u015bci procentowych b\u0142\u0119d\u00f3w bezwzgl\u0119dnych:<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16915 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/mape_interpret3.png\" alt=\"\" width=\"358\" height=\"365\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">MAPE dla tego modelu okazuje si\u0119 wynosi\u0107 <strong>5,12%<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">To m\u00f3wi nam, \u017ce \u015bredni bezwzgl\u0119dny b\u0142\u0105d procentowy mi\u0119dzy sprzeda\u017c\u0105 przewidywan\u0105 przez model a sprzeda\u017c\u0105 rzeczywist\u0105 wynosi <strong>5,12%<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ustalenie, czy jest to <a href=\"https:\/\/statorials.org\/pl\" target=\"_blank\" rel=\"noopener\">dobra warto\u015b\u0107 dla MAPE,<\/a> zale\u017cy od standard\u00f3w bran\u017cowych.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Je\u017celi standardowy przemys\u0142 spo\u017cywczy produkuje model o warto\u015bci MAPE wynosz\u0105cej 2%, w\u00f3wczas warto\u015b\u0107 t\u0119 wynosz\u0105c\u0105 5,12% mo\u017cna uzna\u0107 za wysok\u0105.<\/span><\/p>\n<p> <span style=\"color: #000000;\">I odwrotnie, je\u015bli wi\u0119kszo\u015b\u0107 bran\u017cowych modeli prognoz artyku\u0142\u00f3w spo\u017cywczych generuje warto\u015bci MAPE od 10% do 15%, w\u00f3wczas warto\u015b\u0107 MAPE wynosz\u0105c\u0105 5,12% mo\u017cna uzna\u0107 za nisk\u0105 i model ten mo\u017cna uzna\u0107 za doskona\u0142y do prognozowania przysz\u0142ej sprzeda\u017cy.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Por\u00f3wnanie warto\u015bci MAPE r\u00f3\u017cnych modeli<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">MAPE jest szczeg\u00f3lnie przydatny do por\u00f3wnywania dopasowania r\u00f3\u017cnych modeli.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Za\u0142\u00f3\u017cmy na przyk\u0142ad, \u017ce sie\u0107 spo\u017cywcza chce stworzy\u0107 model do prognozowania przysz\u0142ej sprzeda\u017cy i chce znale\u017a\u0107 najlepszy mo\u017cliwy model spo\u015br\u00f3d kilku potencjalnych modeli.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Za\u0142\u00f3\u017cmy, \u017ce pasuj\u0105 do trzech r\u00f3\u017cnych modeli i znajduj\u0105 odpowiadaj\u0105ce im warto\u015bci MAPE:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">MAPA Modelu 1: <strong>14,5%<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">Model 2 MAPE: <strong>16,7%<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">Model 3 MAPE: <strong>9,8%<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Model 3 ma najni\u017csz\u0105 warto\u015b\u0107 MAPE, co m\u00f3wi nam, \u017ce jest w stanie prognozowa\u0107 przysz\u0142\u0105 sprzeda\u017c z najwi\u0119ksz\u0105 dok\u0142adno\u015bci\u0105 spo\u015br\u00f3d trzech potencjalnych modeli.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Dodatkowe zasoby<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/pl\/karta-excela\/\" target=\"_blank\" rel=\"noopener\">Jak obliczy\u0107 MAPE w Excelu<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/karta-r\/\" target=\"_blank\" rel=\"noopener\">Jak obliczy\u0107 MAPE w R<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/karta-pythona\/\" target=\"_blank\" rel=\"noopener\">Jak obliczy\u0107 MAPE w Pythonie<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/kalkulator-kart\/\" target=\"_blank\" rel=\"noopener\">Kalkulator MAPE<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Jedn\u0105 z najcz\u0119\u015bciej u\u017cywanych metryk do pomiaru dok\u0142adno\u015bci prognozy modelu jest \u015bredni bezwzgl\u0119dny b\u0142\u0105d procentowy , cz\u0119sto w skr\u00f3cie MAPE . Oblicza si\u0119 go w nast\u0119puj\u0105cy spos\u00f3b: MAPE = (1\/n) * \u03a3(|rzeczywista \u2013 prognoza| \/ |rzeczywista|) * 100 Z\u0142oto: \u03a3 \u2013 symbol oznaczaj\u0105cy \u201esum\u0119\u201d n \u2013 wielko\u015b\u0107 pr\u00f3bki current \u2013 Rzeczywista warto\u015b\u0107 danych prognoza \u2013 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1718","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Jak interpretowa\u0107 warto\u015bci MAPE \u2013 Statologia<\/title>\n<meta name=\"description\" content=\"W tym tutorialu na przyk\u0142adzie wyja\u015bniono jak interpretowa\u0107 warto\u015bci MAPE dla danego modelu.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/pl\/jak-interpretowac-mape\/\" \/>\n<meta property=\"og:locale\" content=\"pl_PL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Jak interpretowa\u0107 warto\u015bci MAPE \u2013 Statologia\" \/>\n<meta property=\"og:description\" content=\"W tym tutorialu na przyk\u0142adzie wyja\u015bniono jak interpretowa\u0107 warto\u015bci MAPE dla danego modelu.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pl\/jak-interpretowac-mape\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-25T06:28:26+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/mape_interpret1.png\" \/>\n<meta name=\"author\" content=\"Benjamin Anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Napisane przez\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Szacowany czas czytania\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minuty\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pl\/jak-interpretowac-mape\/\",\"url\":\"https:\/\/statorials.org\/pl\/jak-interpretowac-mape\/\",\"name\":\"Jak interpretowa\u0107 warto\u015bci MAPE \u2013 Statologia\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pl\/#website\"},\"datePublished\":\"2023-07-25T06:28:26+00:00\",\"dateModified\":\"2023-07-25T06:28:26+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\"},\"description\":\"W tym tutorialu na przyk\u0142adzie wyja\u015bniono jak interpretowa\u0107 warto\u015bci MAPE dla danego modelu.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pl\/jak-interpretowac-mape\/#breadcrumb\"},\"inLanguage\":\"pl-PL\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pl\/jak-interpretowac-mape\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pl\/jak-interpretowac-mape\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Dom\",\"item\":\"https:\/\/statorials.org\/pl\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Jak interpretowa\u0107 warto\u015bci mape\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/pl\/#website\",\"url\":\"https:\/\/statorials.org\/pl\/\",\"name\":\"Statorials\",\"description\":\"Tw\u00f3j przewodnik po kompetencjach statystycznych!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/pl\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"pl-PL\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\",\"name\":\"Benjamin Anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pl-PL\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin Anderson\"},\"description\":\"Cze\u015b\u0107, jestem Benjamin i jestem emerytowanym profesorem statystyki, kt\u00f3ry zosta\u0142 oddanym nauczycielem Statorials. 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