{"id":1198,"date":"2023-07-27T07:49:23","date_gmt":"2023-07-27T07:49:23","guid":{"rendered":"https:\/\/statorials.org\/pl\/regresja-lasso-w-pythonie\/"},"modified":"2023-07-27T07:49:23","modified_gmt":"2023-07-27T07:49:23","slug":"regresja-lasso-w-pythonie","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/regresja-lasso-w-pythonie\/","title":{"rendered":"Regresja lasso w pythonie (krok po kroku)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/pl\/regresja-lassa\/\" target=\"_blank\" rel=\"noopener noreferrer\">Regresja Lasso<\/a> to metoda, kt\u00f3rej mo\u017cemy u\u017cy\u0107 do dopasowania modelu regresji, gdy w danych wyst\u0119puje <a href=\"https:\/\/statorials.org\/pl\/regresja-wieloliniowa\/\" target=\"_blank\" rel=\"noopener noreferrer\">wieloliniowo\u015b\u0107<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">W skr\u00f3cie, regresja metod\u0105 najmniejszych kwadrat\u00f3w pr\u00f3buje znale\u017a\u0107 oszacowania wsp\u00f3\u0142czynnik\u00f3w, kt\u00f3re minimalizuj\u0105 rezydualn\u0105 sum\u0119 kwadrat\u00f3w (RSS):<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS = \u03a3(y <sub>i<\/sub> \u2013 \u0177 <sub>i<\/sub> )2<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Z\u0142oto:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> : Grecki symbol oznaczaj\u0105cy <em>sum\u0119<\/em><\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>y <sub>i<\/sub><\/strong> : rzeczywista warto\u015b\u0107 odpowiedzi dla <sup>i-tej<\/sup> obserwacji<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0177 <sub>i<\/sub><\/strong> : Przewidywana warto\u015b\u0107 odpowiedzi na podstawie modelu wielokrotnej regresji liniowej<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">I odwrotnie, regresja lasso ma na celu zminimalizowanie nast\u0119puj\u0105cych element\u00f3w:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS + \u03bb\u03a3|\u03b2 <sub>j<\/sub> |<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">gdzie <em>j<\/em> przechodzi od 1 do <em>p<\/em> zmiennych predykcyjnych i \u03bb \u2265 0.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ten drugi cz\u0142on r\u00f3wnania nazywany jest <em>kar\u0105 za wycofanie<\/em> . W regresji lasso wybieramy warto\u015b\u0107 \u03bb, kt\u00f3ra daje najni\u017cszy mo\u017cliwy test MSE (\u015bredni b\u0142\u0105d kwadratowy).<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ten samouczek zawiera przyk\u0142ad krok po kroku wykonywania regresji lasso w j\u0119zyku Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Krok 1: Zaimportuj niezb\u0119dne pakiety<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Najpierw zaimportujemy niezb\u0119dne pakiety, aby wykona\u0107 regresj\u0119 lasso w Pythonie:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">from<\/span> numpy <span style=\"color: #008000;\">import<\/span> arange\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LassoCV\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Krok 2: Za\u0142aduj dane<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">W tym przyk\u0142adzie u\u017cyjemy zbioru danych o nazwie <strong>mtcars<\/strong> , kt\u00f3ry zawiera informacje o 33 r\u00f3\u017cnych samochodach. U\u017cyjemy <strong>hp<\/strong> jako zmiennej odpowiedzi i nast\u0119puj\u0105cych zmiennych jako predyktor\u00f3w:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">mpg<\/span><\/li>\n<li> <span style=\"color: #000000;\">waga<\/span><\/li>\n<li> <span style=\"color: #000000;\">g\u00f3wno<\/span><\/li>\n<li> <span style=\"color: #000000;\">sek<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak za\u0142adowa\u0107 i wy\u015bwietli\u0107 ten zestaw danych:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define URL where data is located<\/span>\nurl = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data<\/span>\ndata_full = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#select subset of data\n<\/span>data = data_full[[\"mpg\", \"wt\", \"drat\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data<\/span>\ndata[0:6]\n\n\tmpg wt drat qsec hp\n0 21.0 2.620 3.90 16.46 110\n1 21.0 2.875 3.90 17.02 110\n2 22.8 2.320 3.85 18.61 93\n3 21.4 3.215 3.08 19.44 110\n4 18.7 3,440 3.15 17.02 175\n5 18.1 3.460 2.76 20.22 105<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Krok 3: Dopasuj model regresji Lasso<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Nast\u0119pnie u\u017cyjemy funkcji <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.RidgeCV.html\" target=\"_blank\" rel=\"noopener noreferrer\">LassoCV()<\/a> sklearna, aby dopasowa\u0107 model regresji lasso, i u\u017cyjemy funkcji <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.RepeatedKFold.html\" target=\"_blank\" rel=\"noopener noreferrer\">RepeatedKFold()<\/a> do przeprowadzenia k-krotnej walidacji krzy\u017cowej w celu znalezienia optymalnej warto\u015bci alfa do zastosowania dla sk\u0142adnika kary.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><em><strong>Uwaga:<\/strong> w Pythonie zamiast s\u0142owa \u201elambda\u201d u\u017cywany jest termin \u201ealfa\u201d.<\/em><\/span><\/p>\n<p> <span style=\"color: #000000;\">W tym przyk\u0142adzie wybierzemy k = 10 krotno\u015bci i powt\u00f3rzymy proces weryfikacji krzy\u017cowej 3 razy.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Nale\u017cy r\u00f3wnie\u017c pami\u0119ta\u0107, \u017ce LassoCV() domy\u015blnie testuje tylko warto\u015bci alfa 0,1, 1 i 10. Mo\u017cemy jednak ustawi\u0107 w\u0142asny zakres alfa od 0 do 1 w przyrostach co 0,01:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = data[[\"mpg\", \"wt\", \"drat\", \"qsec\"]]\ny = data[\"hp\"]\n\n<span style=\"color: #008080;\">#define cross-validation method to evaluate model\n<\/span>cv = RepeatedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#define model\n<\/span>model = LassoCV(alphas= <span style=\"color: #3366ff;\">arange<\/span> (0, 1, 0.01), cv=cv, n_jobs=<\/strong><\/span> <span style=\"color: #008000;\"><strong>-1<\/strong><\/span> <span style=\"color: #000000;\"><strong>)\n\n<span style=\"color: #008080;\">#fit model\n<\/span>model. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n\n<span style=\"color: #008080;\">#display lambda that produced the lowest test MSE\n<\/span>print( <span style=\"color: #3366ff;\">model.alpha_<\/span> )\n\n0.99<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Warto\u015b\u0107 lambda minimalizuj\u0105ca MSE testu okazuje si\u0119 wynosi\u0107 <strong>0,99<\/strong> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Krok 4: U\u017cyj modelu do przewidywania<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Wreszcie mo\u017cemy u\u017cy\u0107 ostatecznego modelu regresji lassa do przewidywania nowych obserwacji. Na przyk\u0142ad poni\u017cszy kod pokazuje, jak zdefiniowa\u0107 nowy samoch\u00f3d z nast\u0119puj\u0105cymi atrybutami:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">mpg: 24<\/span><\/li>\n<li> <span style=\"color: #000000;\">waga: 2,5<\/span><\/li>\n<li> <span style=\"color: #000000;\">cena: 3,5<\/span><\/li>\n<li> <span style=\"color: #000000;\">sek.: 18,5<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak u\u017cywa\u0107 dopasowanego modelu regresji lasso do przewidywania warto\u015bci <em>hp<\/em> tej nowej obserwacji:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define new observation\n<span style=\"color: #000000;\">new = [24, 2.5, 3.5, 18.5]\n<\/span>\n#predict hp value using lasso regression model\n<span style=\"color: #000000;\">model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n<\/span>\n<span style=\"color: #000000;\">array([105.63442071])\n<\/span><\/span><\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Na podstawie wprowadzonych warto\u015bci model przewiduje, \u017ce ten samoch\u00f3d b\u0119dzie mia\u0142 warto\u015b\u0107 <em>KM<\/em> wynosz\u0105c\u0105 <strong>105,63442071<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Pe\u0142ny kod Pythona u\u017cyty w tym przyk\u0142adzie znajdziesz <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/lasso_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">tutaj<\/a> .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Regresja Lasso to metoda, kt\u00f3rej mo\u017cemy u\u017cy\u0107 do dopasowania modelu regresji, gdy w danych wyst\u0119puje wieloliniowo\u015b\u0107 . W skr\u00f3cie, regresja metod\u0105 najmniejszych kwadrat\u00f3w pr\u00f3buje znale\u017a\u0107 oszacowania wsp\u00f3\u0142czynnik\u00f3w, kt\u00f3re minimalizuj\u0105 rezydualn\u0105 sum\u0119 kwadrat\u00f3w (RSS): RSS = \u03a3(y i \u2013 \u0177 i )2 Z\u0142oto: \u03a3 : Grecki symbol oznaczaj\u0105cy sum\u0119 y i : rzeczywista warto\u015b\u0107 odpowiedzi dla [&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-1198","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>Regresja Lasso w Pythonie (krok po kroku) - Statoriale<\/title>\n<meta name=\"description\" content=\"W tym samouczku wyja\u015bniono, jak wykona\u0107 regresj\u0119 lasso w j\u0119zyku Python, \u0142\u0105cznie z przyk\u0142adem krok po kroku.\" \/>\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\/regresja-lasso-w-pythonie\/\" \/>\n<meta property=\"og:locale\" content=\"pl_PL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Regresja Lasso w Pythonie (krok po kroku) - Statoriale\" \/>\n<meta property=\"og:description\" content=\"W tym samouczku wyja\u015bniono, jak wykona\u0107 regresj\u0119 lasso w j\u0119zyku Python, \u0142\u0105cznie z przyk\u0142adem krok po kroku.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pl\/regresja-lasso-w-pythonie\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T07:49:23+00:00\" \/>\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=\"3 minuty\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pl\/regresja-lasso-w-pythonie\/\",\"url\":\"https:\/\/statorials.org\/pl\/regresja-lasso-w-pythonie\/\",\"name\":\"Regresja Lasso w Pythonie (krok po kroku) - Statoriale\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pl\/#website\"},\"datePublished\":\"2023-07-27T07:49:23+00:00\",\"dateModified\":\"2023-07-27T07:49:23+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\"},\"description\":\"W tym samouczku wyja\u015bniono, jak wykona\u0107 regresj\u0119 lasso w j\u0119zyku Python, \u0142\u0105cznie z przyk\u0142adem krok po kroku.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pl\/regresja-lasso-w-pythonie\/#breadcrumb\"},\"inLanguage\":\"pl-PL\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pl\/regresja-lasso-w-pythonie\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pl\/regresja-lasso-w-pythonie\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Dom\",\"item\":\"https:\/\/statorials.org\/pl\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Regresja lasso w pythonie (krok po kroku)\"}]},{\"@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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