{"id":1155,"date":"2023-07-27T11:31:28","date_gmt":"2023-07-27T11:31:28","guid":{"rendered":"https:\/\/statorials.org\/pt\/regressao-logistica-python\/"},"modified":"2023-07-27T11:31:28","modified_gmt":"2023-07-27T11:31:28","slug":"regressao-logistica-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/pt\/regressao-logistica-python\/","title":{"rendered":"Como realizar regress\u00e3o log\u00edstica em python (passo a passo)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/pt\/regressao-logistica-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">A regress\u00e3o log\u00edstica<\/a> \u00e9 um m\u00e9todo que podemos usar para ajustar um modelo de regress\u00e3o quando a <a href=\"https:\/\/statorials.org\/pt\/respostas-explicativas-das-variaveis\/\" target=\"_blank\" rel=\"noopener noreferrer\">vari\u00e1vel de resposta<\/a> \u00e9 bin\u00e1ria.<\/span><\/p>\n<p> <span style=\"color: #000000;\">A regress\u00e3o log\u00edstica usa um m\u00e9todo conhecido como <em>estimativa de m\u00e1xima verossimilhan\u00e7a<\/em> para encontrar uma equa\u00e7\u00e3o da seguinte forma:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>log[p(X) \/ ( <sub>1<\/sub> -p(X))] = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> X <sub>1<\/sub> + \u03b2 <sub>2<\/sub> X <sub>2<\/sub> +\u2026 + \u03b2 <sub>p<\/sub><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Ouro:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>X <sub>j<\/sub><\/strong> : a j- <sup>\u00e9sima<\/sup> vari\u00e1vel preditiva<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u03b2 <sub>j<\/sub><\/strong> : estimativa do coeficiente para a j <sup>-\u00e9sima<\/sup> vari\u00e1vel preditiva<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">A f\u00f3rmula no lado direito da equa\u00e7\u00e3o prev\u00ea o <strong>log de probabilidade<\/strong> de que a vari\u00e1vel de resposta assuma o valor 1.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Assim, quando ajustamos um modelo de regress\u00e3o log\u00edstica, podemos usar a seguinte equa\u00e7\u00e3o para calcular a probabilidade de uma determinada observa\u00e7\u00e3o assumir o valor 1:<\/span><\/p>\n<p> <span style=\"color: #000000;\">p(X) = e <sup>\u03b2 <sub>0<\/sub> + <sub>\u03b2<\/sub> <sub>1<\/sub> <sub>X<\/sub> <sub>1<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> <sup><sub>2<\/sub> <sub>X<\/sub> <sub>2<\/sub> <sub>+<\/sub> <sub>\u2026<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> p<\/span><\/p>\n<p> <span style=\"color: #000000;\">Em seguida, usamos um certo limite de probabilidade para classificar a observa\u00e7\u00e3o como 1 ou 0.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Por exemplo, poder\u00edamos dizer que as observa\u00e7\u00f5es com probabilidade maior ou igual a 0,5 ser\u00e3o classificadas como \u201c1\u201d e todas as outras observa\u00e7\u00f5es ser\u00e3o classificadas como \u201c0\u201d.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Este tutorial fornece um exemplo passo a passo de como realizar regress\u00e3o log\u00edstica em R.<\/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;\">Primeiramente, importaremos os pacotes necess\u00e1rios para realizar a regress\u00e3o log\u00edstica em Python:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LogisticRegression\n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> metrics\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Etapa 2: carregar dados<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Para este exemplo, usaremos o conjunto de dados <strong>padr\u00e3o<\/strong> do <a href=\"https:\/\/www.ime.unicamp.br\/~dias\/Intoduction%20to%20Statistical%20Learning.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">livro Introdu\u00e7\u00e3o ao Aprendizado Estat\u00edstico<\/a> . Podemos usar o seguinte c\u00f3digo para carregar e exibir um resumo do conjunto de dados:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#import dataset from CSV file on Github\n<span style=\"color: #000000;\">url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/default.csv\"\ndata = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n<\/span><\/span>\n<span style=\"color: #008080;\">#view first six rows of dataset\n<\/span>data[0:6]\n\n        default student balance income\n0 0 0 729.526495 44361.625074\n1 0 1 817.180407 12106.134700\n2 0 0 1073.549164 31767.138947\n3 0 0 529.250605 35704.493935\n4 0 0 785.655883 38463.495879\n5 0 1 919.588530 7491.558572  \n\n<span style=\"color: #008080;\">#find total observations in dataset<\/span>\nlen( <span style=\"color: #3366ff;\">data.index<\/span> )\n\n10000\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Este conjunto de dados cont\u00e9m as seguintes informa\u00e7\u00f5es sobre 10.000 indiv\u00edduos:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>inadimpl\u00eancia:<\/strong> indica se um indiv\u00edduo est\u00e1 inadimplente ou n\u00e3o.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>estudante:<\/strong> indica se um indiv\u00edduo \u00e9 estudante ou n\u00e3o.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>saldo:<\/strong> Saldo m\u00e9dio mantido por um indiv\u00edduo.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>renda:<\/strong> Renda da pessoa f\u00edsica.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Usaremos a situa\u00e7\u00e3o estudantil, o saldo banc\u00e1rio e a renda para construir um modelo de regress\u00e3o log\u00edstica que prev\u00ea a probabilidade de inadimpl\u00eancia de um determinado indiv\u00edduo.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Etapa 3: criar amostras de treinamento e teste<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">A seguir, dividiremos o conjunto de dados em um conjunto de treinamento para <em>treinar<\/em> o modelo e um conjunto de teste <em>para testar<\/em> o modelo.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define the predictor variables and the response variable\n<\/span>X = data[[' <span style=\"color: #008000;\">student<\/span> ',' <span style=\"color: #008000;\">balance<\/span> ',' <span style=\"color: #008000;\">income<\/span> ']]\ny = data[' <span style=\"color: #008000;\">default<\/span> ']\n\n<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=0.3,random_state=0)<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Passo 4: Ajustar o modelo de regress\u00e3o log\u00edstica<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">A seguir, usaremos a fun\u00e7\u00e3o <b>LogisticRegression()<\/b> para ajustar um modelo de regress\u00e3o log\u00edstica ao conjunto de dados:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#instantiate the model\n<\/span>log_regression = LogisticRegression()\n\n<span style=\"color: #008080;\">#fit the model using the training data\n<\/span>log_regression. <span style=\"color: #3366ff;\">fit<\/span> (X_train,y_train)\n\n<span style=\"color: #008080;\">#use model to make predictions on test data\n<\/span>y_pred = log_regression. <span style=\"color: #3366ff;\">predict<\/span> (X_test)\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Etapa 5: diagn\u00f3stico do modelo<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Depois de ajustar o modelo de regress\u00e3o, podemos analisar o desempenho do nosso modelo no conjunto de dados de teste.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Primeiro, criaremos a matriz de confus\u00e3o<\/span> <span style=\"color: #000000;\">para o modelo:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>cnf_matrix = metrics. <span style=\"color: #3366ff;\">confusion_matrix<\/span> (y_test, y_pred)\ncnf_matrix\n\narray([[2886, 1],\n       [113,0]])\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Da matriz de confus\u00e3o podemos ver que:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">#Verdadeiras previs\u00f5es positivas: 2.886<\/span><\/li>\n<li> <span style=\"color: #000000;\">#Previs\u00f5es negativas verdadeiras: 0<\/span><\/li>\n<li> <span style=\"color: #000000;\">#Previs\u00f5es falsas positivas: 113<\/span><\/li>\n<li> <span style=\"color: #000000;\">#Previs\u00f5es falsas negativas: 1<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Tamb\u00e9m podemos obter o modelo de precis\u00e3o, que nos informa a porcentagem de previs\u00f5es de corre\u00e7\u00e3o feitas pelo modelo:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>print(\" <span style=\"color: #008000;\">Accuracy:<\/span> \", <span style=\"color: #3366ff;\">metrics.accuracy_score<\/span> (y_test, y_pred))l\n\nAccuracy: 0.962\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Isso nos diz que o modelo fez a previs\u00e3o correta sobre se um indiv\u00edduo entraria em default ou n\u00e3o <strong>em 96,2%<\/strong> das vezes.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Finalmente, podemos tra\u00e7ar a curva Receiver Operating Characteristic (ROC), que exibe a porcentagem de verdadeiros positivos previstos pelo modelo quando o limite de probabilidade de predi\u00e7\u00e3o \u00e9 reduzido de 1 para 0.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Quanto maior a AUC (\u00e1rea sob a curva), mais precisamente o nosso modelo \u00e9 capaz de prever os resultados:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define metrics<\/span>\ny_pred_proba = log_regression. <span style=\"color: #3366ff;\">predict_proba<\/span> (X_test)[::,1]\nfpr, tpr, _ = metrics. <span style=\"color: #3366ff;\">roc_curve<\/span> (y_test, y_pred_proba)\nauc = metrics. <span style=\"color: #3366ff;\">roc_auc_score<\/span> (y_test, y_pred_proba)\n\n<span style=\"color: #008080;\">#create ROC curve\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (fpr,tpr,label=\" <span style=\"color: #008000;\">AUC=<\/span> \"+str(auc))\nplt. <span style=\"color: #3366ff;\">legend<\/span> (loc=4)\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/strong><\/span><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11591 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/auc1.png\" alt=\"Curva ROC em Python\" width=\"389\" height=\"262\" srcset=\"\" sizes=\"auto, \"><\/p>\n<div class=\"entry-content entry-content-single\" data-content-ads-inserted=\"true\">\n<p> <em><span style=\"color: #000000;\">O c\u00f3digo Python completo usado neste tutorial pode ser encontrado <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/logistic_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">aqui<\/a> .<\/span><\/em><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>A regress\u00e3o log\u00edstica \u00e9 um m\u00e9todo que podemos usar para ajustar um modelo de regress\u00e3o quando a vari\u00e1vel de resposta \u00e9 bin\u00e1ria. A regress\u00e3o log\u00edstica usa um m\u00e9todo conhecido como estimativa de m\u00e1xima verossimilhan\u00e7a para encontrar uma equa\u00e7\u00e3o da seguinte forma: log[p(X) \/ ( 1 -p(X))] = \u03b2 0 + \u03b2 1 X 1 + [&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-1155","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>Como realizar regress\u00e3o log\u00edstica em Python (passo a passo) - Estatologia<\/title>\n<meta name=\"description\" content=\"Este tutorial explica como realizar regress\u00e3o log\u00edstica 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-logistica-python\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Como realizar regress\u00e3o log\u00edstica em Python (passo a passo) - Estatologia\" \/>\n<meta property=\"og:description\" content=\"Este tutorial explica como realizar regress\u00e3o log\u00edstica em Python, incluindo um exemplo passo a passo.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pt\/regressao-logistica-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T11:31:28+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/auc1.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=\"4 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pt\/regressao-logistica-python\/\",\"url\":\"https:\/\/statorials.org\/pt\/regressao-logistica-python\/\",\"name\":\"Como realizar regress\u00e3o log\u00edstica em Python (passo a passo) - Estatologia\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pt\/#website\"},\"datePublished\":\"2023-07-27T11:31:28+00:00\",\"dateModified\":\"2023-07-27T11:31:28+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666\"},\"description\":\"Este tutorial explica como realizar regress\u00e3o log\u00edstica em Python, incluindo um exemplo passo a passo.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pt\/regressao-logistica-python\/#breadcrumb\"},\"inLanguage\":\"pt-PT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pt\/regressao-logistica-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pt\/regressao-logistica-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Lar\",\"item\":\"https:\/\/statorials.org\/pt\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Como realizar regress\u00e3o log\u00edstica 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. 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