{"id":1205,"date":"2023-07-27T07:20:18","date_gmt":"2023-07-27T07:20:18","guid":{"rendered":"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/"},"modified":"2023-07-27T07:20:18","modified_gmt":"2023-07-27T07:20:18","slug":"regressao-de-componentes-principais-em-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/","title":{"rendered":"Regress\u00e3o de componentes principais em python (passo a passo)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Dado um conjunto de <em>p<\/em> vari\u00e1veis preditoras e uma vari\u00e1vel de resposta, <a href=\"https:\/\/statorials.org\/pt\/regressao-linear-multipla\/\" target=\"_blank\" rel=\"noopener noreferrer\">a regress\u00e3o linear m\u00faltipla<\/a> usa um m\u00e9todo conhecido como m\u00ednimos quadrados para minimizar a soma residual dos quadrados (RSS):<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS = \u03a3(y <sub>i<\/sub> \u2013 \u0177 <sub>i<\/sub> ) <sup>2<\/sup><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Ouro:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> : Um s\u00edmbolo grego que significa <em>soma<\/em><\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>y <sub>i<\/sub><\/strong> : o valor real da resposta para a <sup>i-\u00e9sima<\/sup> observa\u00e7\u00e3o<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0177 <sub>i<\/sub><\/strong> : O valor da resposta prevista com base no modelo de regress\u00e3o linear m\u00faltipla<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">No entanto, quando as vari\u00e1veis preditoras s\u00e3o altamente correlacionadas,<\/span> <a href=\"https:\/\/statorials.org\/pt\/regressao-multicolinearidade\/\" target=\"_blank\" rel=\"noopener noreferrer\">a multicolinearidade<\/a> <span style=\"color: #000000;\">pode se tornar um problema. Isso pode tornar as estimativas dos coeficientes do modelo pouco confi\u00e1veis e exibir alta vari\u00e2ncia.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Uma maneira de evitar esse problema \u00e9 usar <a href=\"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais\/\" target=\"_blank\" rel=\"noopener noreferrer\">a regress\u00e3o de componentes principais<\/a> , que encontra <em>M<\/em> combina\u00e7\u00f5es lineares (chamadas de &#8220;componentes principais&#8221;) dos <em>p<\/em> preditores originais e, em seguida, usa m\u00ednimos quadrados para ajustar um modelo de regress\u00e3o linear usando os componentes principais como preditores.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Este tutorial fornece um exemplo passo a passo de como realizar a regress\u00e3o de componentes principais em Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Passo 1: Importe os pacotes necess\u00e1rios<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Primeiro, importaremos os pacotes necess\u00e1rios para realizar a regress\u00e3o de componentes principais (PCR) em Python:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> scale \n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> model_selection\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold\n<span style=\"color: #008000;\">from<\/span> sklearn.model_selection <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from<\/span> sklearn. PCA <span style=\"color: #008000;\">import<\/span> <span style=\"color: #3366ff;\">decomposition<\/span>\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LinearRegression\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">metrics<\/span> <span style=\"color: #008000;\">import<\/span> mean_squared_error\n<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Etapa 2: carregar dados<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Neste exemplo, usaremos um conjunto de dados chamado <strong>mtcars<\/strong> , que cont\u00e9m informa\u00e7\u00f5es sobre 33 carros diferentes. Usaremos <strong>hp<\/strong> como vari\u00e1vel de resposta e as seguintes vari\u00e1veis como preditores:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">mpg<\/span><\/li>\n<li> <span style=\"color: #000000;\">mostrar<\/span><\/li>\n<li> <span style=\"color: #000000;\">merda<\/span><\/li>\n<li> <span style=\"color: #000000;\">peso<\/span><\/li>\n<li> <span style=\"color: #000000;\">qsec<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">O c\u00f3digo a seguir mostra como carregar e exibir esse conjunto de dados:<\/span><\/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\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data\n<\/span>data_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\", \"disp\", \"drat\", \"wt\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data\n<\/span>data[0:6]\n\n\n        mpg disp drat wt qsec hp\n0 21.0 160.0 3.90 2.620 16.46 110\n1 21.0 160.0 3.90 2.875 17.02 110\n2 22.8 108.0 3.85 2.320 18.61 93\n3 21.4 258.0 3.08 3.215 19.44 110\n4 18.7 360.0 3.15 3.440 17.02 175\n5 18.1 225.0 2.76 3.460 20.22 105<\/strong><\/span><\/pre>\n<h3> <strong>Passo 3: Ajustar o modelo PCR<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">O c\u00f3digo a seguir mostra como ajustar o modelo PCR a esses dados. Observe o seguinte:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>pca.fit_transform(scale(X))<\/strong> : Isso diz ao Python que cada uma das vari\u00e1veis preditoras deve ser dimensionada para ter uma m\u00e9dia de 0 e um desvio padr\u00e3o de 1. Isso garante que nenhuma vari\u00e1vel preditora tenha muita influ\u00eancia no modelo se isto ocorre. ser medido em unidades diferentes.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>cv = RepeatedKFold()<\/strong> : diz ao Python para usar <a href=\"https:\/\/statorials.org\/pt\/validacao-cruzada-k-fold\/\" target=\"_blank\" rel=\"noopener noreferrer\">a valida\u00e7\u00e3o cruzada k-fold<\/a> para avaliar o desempenho do modelo. Para este exemplo escolhemos k = 10 dobras, repetidas 3 vezes.<\/span> <\/li>\n<\/ul>\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\", \"disp\", \"drat\", \"wt\", \"qsec\"]]\ny = data[[\"hp\"]]\n\n<span style=\"color: #008080;\">#scale predictor variables\n<\/span>pca = pca()\nX_reduced = pca. <span style=\"color: #3366ff;\">fit_transform<\/span> ( <span style=\"color: #3366ff;\">scale<\/span> (X))\n\n<span style=\"color: #008080;\">#define cross validation method\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\nregr = LinearRegression()\nmse = []\n\n<span style=\"color: #008080;\"># Calculate MSE with only the intercept\n<\/span>score = -1*model_selection. <span style=\"color: #3366ff;\">cross_val_score<\/span> (regr,\n           n.p. <span style=\"color: #3366ff;\">ones<\/span> ((len(X_reduced),1)), y, cv=cv,\n           scoring=' <span style=\"color: #008000;\">neg_mean_squared_error<\/span> '). <span style=\"color: #3366ff;\">mean<\/span> ()    \nmse. <span style=\"color: #3366ff;\">append<\/span> (score)\n\n<span style=\"color: #008080;\"># Calculate MSE using cross-validation, adding one component at a time\n<\/span><span style=\"color: #008000;\">for<\/span> i <span style=\"color: #008000;\">in<\/span> np. <span style=\"color: #3366ff;\">arange<\/span> (1, 6):\n    score = -1*model_selection. <span style=\"color: #3366ff;\">cross_val_score<\/span> (regr,\n               X_reduced[:,:i], y, cv=cv, scoring=' <span style=\"color: #008000;\">neg_mean_squared_error<\/span> '). <span style=\"color: #3366ff;\">mean<\/span> ()\n    mse. <span style=\"color: #3366ff;\">append<\/span> (score)\n    \n<span style=\"color: #008080;\"># Plot cross-validation results    \n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (mse)\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> ('Number of Principal Components')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> ('MSE')\nplt. <span style=\"color: #3366ff;\">title<\/span> ('hp')<\/strong><\/span> <\/pre>\n<h3><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11950 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/pcrpython1.png\" alt=\"Regress\u00e3o de componentes principais em Python\" width=\"424\" height=\"285\" srcset=\"\" sizes=\"auto, \"><\/h3>\n<p> <span style=\"color: #000000;\">O gr\u00e1fico exibe o n\u00famero de componentes principais ao longo do eixo x e o teste MSE (erro quadr\u00e1tico m\u00e9dio) ao longo do eixo y.<\/span><\/p>\n<p> <span style=\"color: #000000;\">No gr\u00e1fico, podemos ver que o MSE do teste diminui ao adicionar dois componentes principais, mas come\u00e7a a aumentar \u00e0 medida que adicionamos mais de dois componentes principais.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Assim, o modelo \u00f3timo inclui apenas os dois primeiros componentes principais.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tamb\u00e9m podemos usar o c\u00f3digo a seguir para calcular a porcentagem de vari\u00e2ncia na vari\u00e1vel de resposta explicada pela adi\u00e7\u00e3o de cada componente principal ao modelo:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>n.p. <span style=\"color: #3366ff;\">cumsum<\/span> (np. <span style=\"color: #3366ff;\">round<\/span> (pca. <span style=\"color: #3366ff;\">explained_variance_ratio_<\/span> , decimals= <span style=\"color: #008000;\">4<\/span> )* <span style=\"color: #008000;\">100<\/span> )\n\narray([69.83, 89.35, 95.88, 98.95, 99.99])\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Podemos ver o seguinte:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Utilizando apenas a primeira componente principal, podemos explicar <strong>69,83%<\/strong> da varia\u00e7\u00e3o da vari\u00e1vel resposta.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Adicionando o segundo componente principal, podemos explicar <strong>89,35%<\/strong> da varia\u00e7\u00e3o da vari\u00e1vel resposta.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Observe que ainda seremos capazes de explicar mais vari\u00e2ncia usando mais componentes principais, mas podemos ver que adicionar mais de dois componentes principais n\u00e3o aumenta muito a porcentagem de vari\u00e2ncia explicada.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Etapa 4: use o modelo final para fazer previs\u00f5es<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Podemos usar o modelo final de PCR de dois componentes principais para fazer previs\u00f5es sobre novas observa\u00e7\u00f5es.<\/span><\/p>\n<p> <span style=\"color: #000000;\">O c\u00f3digo a seguir mostra como dividir o conjunto de dados original em um conjunto de treinamento e teste e usar o modelo PCR com dois componentes principais para fazer previs\u00f5es no conjunto de teste.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#split the dataset into training (70%) and testing (30%) sets\n<\/span>X_train,X_test,y_train,y_test = <span style=\"color: #3366ff;\">train_test_split<\/span> (X,y,test_size= <span style=\"color: #008000;\">0.3<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> ) \n\n<span style=\"color: #008080;\">#scale the training and testing data\n<\/span>X_reduced_train = pca. <span style=\"color: #3366ff;\">fit_transform<\/span> ( <span style=\"color: #3366ff;\">scale<\/span> (X_train))\nX_reduced_test = pca. <span style=\"color: #3366ff;\">transform<\/span> ( <span style=\"color: #3366ff;\">scale<\/span> (X_test))[:,:1]\n\n<span style=\"color: #008080;\">#train PCR model on training data \n<\/span>regr = LinearRegression()\nreg. <span style=\"color: #3366ff;\">fit<\/span> (X_reduced_train[:,:1], y_train)\n\n<span style=\"color: #008080;\">#calculate RMSE\n<\/span>pred = regr. <span style=\"color: #3366ff;\">predict<\/span> (X_reduced_test)\nn.p. <span style=\"color: #3366ff;\">sqrt<\/span> ( <span style=\"color: #3366ff;\">mean_squared_error<\/span> (y_test, pred))\n\n40.2096\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Vemos que o teste RMSE acabou sendo <strong>40,2096<\/strong> . Este \u00e9 o desvio m\u00e9dio entre o valor <em>de HP<\/em> previsto e o valor <em>de HP<\/em> observado para as observa\u00e7\u00f5es do conjunto de teste.<\/span><\/p>\n<p> <span style=\"color: #000000;\">O c\u00f3digo Python completo usado neste exemplo pode ser encontrado <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/principal_components_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">aqui<\/a> .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Dado um conjunto de p vari\u00e1veis preditoras e uma vari\u00e1vel de resposta, a regress\u00e3o linear m\u00faltipla usa um m\u00e9todo conhecido como m\u00ednimos quadrados para minimizar a soma residual dos quadrados (RSS): RSS = \u03a3(y i \u2013 \u0177 i ) 2 Ouro: \u03a3 : Um s\u00edmbolo grego que significa soma y i : o valor real [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1205","post","type-post","status-publish","format-standard","hentry","category-guia"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Regress\u00e3o de componentes principais em Python (passo a passo)<\/title>\n<meta name=\"description\" content=\"Este tutorial explica como realizar a regress\u00e3o de componentes principais em Python, incluindo um exemplo passo a passo.\" \/>\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\/pt\/regressao-de-componentes-principais-em-python\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Regress\u00e3o de componentes principais em Python (passo a passo)\" \/>\n<meta property=\"og:description\" content=\"Este tutorial explica como realizar a regress\u00e3o de componentes principais em Python, incluindo um exemplo passo a passo.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T07:20:18+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/pcrpython1.png\" \/>\n<meta name=\"author\" content=\"Dr. benjamim anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dr. benjamim anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tempo estimado de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/\",\"url\":\"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/\",\"name\":\"Regress\u00e3o de componentes principais em Python (passo a passo)\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pt\/#website\"},\"datePublished\":\"2023-07-27T07:20:18+00:00\",\"dateModified\":\"2023-07-27T07:20:18+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666\"},\"description\":\"Este tutorial explica como realizar a regress\u00e3o de componentes principais em Python, incluindo um exemplo passo a passo.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/#breadcrumb\"},\"inLanguage\":\"pt-PT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Lar\",\"item\":\"https:\/\/statorials.org\/pt\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Regress\u00e3o de componentes principais em python (passo a passo)\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/pt\/#website\",\"url\":\"https:\/\/statorials.org\/pt\/\",\"name\":\"Statorials\",\"description\":\"O seu guia para a literacia estat\u00edstica!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/pt\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"pt-PT\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666\",\"name\":\"Dr. benjamim anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-PT\",\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/pt\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/pt\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Dr. benjamim anderson\"},\"description\":\"Ol\u00e1, sou Benjamin, um professor aposentado de estat\u00edstica que se tornou professor dedicado na Statorials. Com vasta experi\u00eancia e conhecimento na \u00e1rea de estat\u00edstica, estou empenhado em compartilhar meu conhecimento para capacitar os alunos por meio de Statorials. Saber mais\",\"sameAs\":[\"https:\/\/statorials.org\/pt\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Regress\u00e3o de componentes principais em Python (passo a passo)","description":"Este tutorial explica como realizar a regress\u00e3o de componentes principais em Python, incluindo um exemplo passo a passo.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/","og_locale":"pt_PT","og_type":"article","og_title":"Regress\u00e3o de componentes principais em Python (passo a passo)","og_description":"Este tutorial explica como realizar a regress\u00e3o de componentes principais em Python, incluindo um exemplo passo a passo.","og_url":"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/","og_site_name":"Statorials","article_published_time":"2023-07-27T07:20:18+00:00","og_image":[{"url":"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/pcrpython1.png"}],"author":"Dr. benjamim anderson","twitter_card":"summary_large_image","twitter_misc":{"Escrito por":"Dr. benjamim anderson","Tempo estimado de leitura":"5 minutos"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/","url":"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/","name":"Regress\u00e3o de componentes principais em Python (passo a passo)","isPartOf":{"@id":"https:\/\/statorials.org\/pt\/#website"},"datePublished":"2023-07-27T07:20:18+00:00","dateModified":"2023-07-27T07:20:18+00:00","author":{"@id":"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666"},"description":"Este tutorial explica como realizar a regress\u00e3o de componentes principais em Python, incluindo um exemplo passo a passo.","breadcrumb":{"@id":"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/#breadcrumb"},"inLanguage":"pt-PT","potentialAction":[{"@type":"ReadAction","target":["https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/statorials.org\/pt\/regressao-de-componentes-principais-em-python\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Lar","item":"https:\/\/statorials.org\/pt\/"},{"@type":"ListItem","position":2,"name":"Regress\u00e3o de componentes principais em python (passo a passo)"}]},{"@type":"WebSite","@id":"https:\/\/statorials.org\/pt\/#website","url":"https:\/\/statorials.org\/pt\/","name":"Statorials","description":"O seu guia para a literacia estat\u00edstica!","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/statorials.org\/pt\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"pt-PT"},{"@type":"Person","@id":"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666","name":"Dr. benjamim anderson","image":{"@type":"ImageObject","inLanguage":"pt-PT","@id":"https:\/\/statorials.org\/pt\/#\/schema\/person\/image\/","url":"https:\/\/statorials.org\/pt\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","contentUrl":"https:\/\/statorials.org\/pt\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","caption":"Dr. benjamim anderson"},"description":"Ol\u00e1, sou Benjamin, um professor aposentado de estat\u00edstica que se tornou professor dedicado na Statorials. Com vasta experi\u00eancia e conhecimento na \u00e1rea de estat\u00edstica, estou empenhado em compartilhar meu conhecimento para capacitar os alunos por meio de Statorials. Saber mais","sameAs":["https:\/\/statorials.org\/pt"]}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"_links":{"self":[{"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/posts\/1205","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/comments?post=1205"}],"version-history":[{"count":0,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/posts\/1205\/revisions"}],"wp:attachment":[{"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/media?parent=1205"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/categories?post=1205"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statorials.org\/pt\/wp-json\/wp\/v2\/tags?post=1205"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}