{"id":4938,"date":"2017-12-18T10:54:33","date_gmt":"2017-12-18T10:54:33","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=4938"},"modified":"2021-08-25T17:25:57","modified_gmt":"2021-08-25T11:55:57","slug":"logistic-regression-in-r","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/logistic-regression-in-r\/","title":{"rendered":"Logistic Regression in R &#8211; A Detailed Guide for Beginners!"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:2049,&quot;href&quot;:&quot;https:\\\/\\\/www.r-project.org\\\/bugs.html&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20250923123728\\\/https:\\\/\\\/www.r-project.org\\\/bugs.html&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-11 00:01:11&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2025-12-14 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05:36:52&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-02 15:57:53&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-06 10:14:50&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-09 15:07:27&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-13 07:26:05&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-16 07:34:31&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-19 14:33:23&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-22 20:54:41&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-26 03:30:08&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-29 06:56:00&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-06-02 15:32:45&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-06-06 15:33:52&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-06-09 22:43:08&quot;,&quot;http_code&quot;:503}],&quot;broken&quot;:true,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-09 22:43:08&quot;,&quot;http_code&quot;:503},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p>In this tutorial, we will learn about the concept of logistic regression in R along with its syntax and parameters. We will also build a logistic regression model and explore its derivation, performance and applications.<\/p>\n<p>Let&#8217;s quickly begin the tutorial.<\/p>\n<div class=\"\">\n<h2 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">What is Logistic Regression in R?<\/h2>\n<\/div>\n<p><span style=\"font-weight: 400\">In logistic regression, we fit a regression curve,<em> y = f(x)<\/em> where y represents a categorical variable. This model is used to predict that y has given a set of predictors x. Hence, the <em>predictors can be continuous, categorical or a mix of both.<\/em><\/span><\/p>\n<p><span style=\"font-weight: 400\">It is a classification algorithm which comes under nonlinear regression. We use it to predict a binary outcome (<em>1\/ 0, Yes\/ No, True\/ False<\/em>) given as a set of independent variables. Moreover, it helps to represent binary\/categorical outcome by using dummy variables.<\/span><\/p>\n<p><span style=\"font-weight: 400\">It is a regression model in which the response variable has categorical values such as True\/False or 0\/1. Therefore, we are able to measure the probability of the binary response.\u00a0<\/span><\/p>\n<p><em><strong>Make sure that you have completed &#8211; <a href=\"https:\/\/data-flair.training\/blogs\/r-nonlinear-regression\/\">R Nonlinear Regression Analysis<\/a><\/strong><\/em><\/p>\n<h4>Syntax and Expression of R Logistic Regression<\/h4>\n<p><span style=\"font-weight: 400\">The general mathematical equation for logistic regression is:<\/span><\/p>\n<p style=\"text-align: center\"><em><span style=\"font-weight: 400\">y = 1\/(1+e^-(a+b1x1+b2x2+b3x3+&#8230;))<\/span><\/em><\/p>\n<p><b>Following is the description of the parameters used:<\/b><\/p>\n<ul>\n<li><span style=\"font-weight: 400\">y is the response variable.<\/span><\/li>\n<li><span style=\"font-weight: 400\">x is the predictor variable.<\/span><\/li>\n<li><span style=\"font-weight: 400\">a and b are the coefficients which are numeric constants.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">We use the <em>glm()<\/em> function to create the regression model and also get its summary for analysis.<\/span><\/p>\n<p><b>The syntax of logistic Regression in R:<\/b><\/p>\n<p>The basic syntax for glm() function in logistic regression is:<\/p>\n<p style=\"text-align: center\"><em><span style=\"font-weight: 400\">glm(formula,data,family)<\/span><\/em><\/p>\n<p><b>Description of the parameters used:<\/b><\/p>\n<ul>\n<li><span style=\"font-weight: 400\"><strong>Formula &#8211;<\/strong>\u00a0Presenting the relationship between the variables.<\/span><\/li>\n<li><span style=\"font-weight: 400\"><strong>Data<\/strong> is the dataset giving the values of these variables.<\/span><\/li>\n<li><span style=\"font-weight: 400\">The <strong>family<\/strong> is the R object to specify the details of the model. Also, its value is binomial for logistic regression.<\/span><\/li>\n<\/ul>\n<h3>Derivation of Logistic Regression in R<\/h3>\n<p>We use a generalized model as a larger class of algorithms. Basically, this model was proposed by Nelder and Wedderburn in 1972.<\/p>\n<p><span style=\"font-weight: 400\">The fundamental equation of generalized linear model is:<\/span><br \/>\n<b><\/b><\/p>\n<p style=\"text-align: center\"><em>g(E(y)) = \u03b1 + \u03b2x1 + \u03b3x2<\/em><\/p>\n<p><span style=\"font-weight: 400\">Here, <strong>g()<\/strong> is the <strong>link function<\/strong>;<\/span><\/p>\n<p><span style=\"font-weight: 400\"><strong>E(y)<\/strong> is the <strong>expectation of target variable<\/strong>, and <\/span><\/p>\n<p><span style=\"font-weight: 400\"><strong>\u03b1 + \u03b2x1 + \u03b3x2<\/strong> is the <strong>linear predictor<\/strong>. <\/span><\/p>\n<p><span style=\"font-weight: 400\">The role of the link function is to \u2018link\u2019 the expectation of y to linear predictor.<\/span><\/p>\n<p><em><strong>You must definitely check the <a href=\"https:\/\/data-flair.training\/blogs\/r-linear-regression-tutorial\/\">Multiple Linear Regression in R<\/a><\/strong><\/em><\/p>\n<h3>Performance of Logistic Regression Model<\/h3>\n<p>To test the performance of this model, we must consider a few metrics. Irrespective of a tool (SAS, R or Python) you would work on, always look for:<\/p>\n<h4>1. AIC (Akaike Information Criteria)<\/h4>\n<p><span style=\"font-weight: 400\">In logistic regression, AIC is the analogous metric of adjusted R\u00b2. Thus, we always prefer the model with the smallest AIC value.<\/span><\/p>\n<h4>2. Null Deviance and Residual Deviance<\/h4>\n<ul>\n<li><strong>Null Deviance<\/strong><\/li>\n<\/ul>\n<p>In null deviance, the response that is predicted by the model is just an intercept.<\/p>\n<ul>\n<li><strong>Residual Deviance<\/strong><\/li>\n<\/ul>\n<p>It indicates the response predicted by a model of adding independent variables.<\/p>\n<h4>3. Confusion Matrix<\/h4>\n<p>It is a type of matrix in which we represent a tabular representation of Actual vs Predicted values. Also, this helps us to find the accuracy of the model and avoid over-fitting.<\/p>\n<p>Any queries in R Logistic Regression till now? Share your views in the comment section below.<\/p>\n<p><em><strong>Find out the best tool for Data Science Learning &#8211; <a href=\"https:\/\/data-flair.training\/blogs\/r-python-or-sas-for-data-science\/\">R, Python or SAS<\/a><\/strong><\/em><\/p>\n<h2>Building Logistic Regression Model in R<\/h2>\n<p>In this section, we will build our logistic regression model using the BreastCancer dataset that is available by default in <a href=\"https:\/\/www.r-project.org\/bugs.html\">R<\/a>. We will start by importing the data and displaying the information related to it with the <em>str()<\/em> function:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt; data(BreastCancer, package = \"mlbench\")  #Author DataFlair\r\n&gt; b_canc = BreastCancer[complete.cases(BreastCancer),]\r\n&gt; str(b_canc)\r\n<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/data-b_canc-Logistic-Regression-1-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63666\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/data-b_canc-Logistic-Regression-1-1.png\" alt=\"data-b_canc-Logistic-Regression\" width=\"1298\" height=\"743\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/data-b_canc-Logistic-Regression-1-1.png 1298w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/data-b_canc-Logistic-Regression-1-1-150x86.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/data-b_canc-Logistic-Regression-1-1-300x172.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/data-b_canc-Logistic-Regression-1-1-768x440.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/data-b_canc-Logistic-Regression-1-1-1024x586.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/data-b_canc-Logistic-Regression-1-1-520x298.png 520w\" sizes=\"auto, (max-width: 1298px) 100vw, 1298px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400\">We now split our data into training and test set with the training set holding 70% of the data and test set comprising of the remaining percentage.<\/span><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt; #Author DataFlair\r\n&gt; set.seed(100)\r\n&gt; Train_Ratio &lt;- createDataPartition(b_canc$Class, p=0.7, list = F)\r\n&gt; Train_Data &lt;- b_canc[Train_Ratio, ]\r\n&gt; Test_Data &lt;- b_canc[-Train_Ratio, ]\r\n<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/set.seed-train-ratio-Logistic-Regression.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63668\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/set.seed-train-ratio-Logistic-Regression.png\" alt=\"set.seed-train-ratio-Logistic-Regression\" width=\"1298\" height=\"740\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/set.seed-train-ratio-Logistic-Regression.png 1298w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/set.seed-train-ratio-Logistic-Regression-150x86.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/set.seed-train-ratio-Logistic-Regression-300x171.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/set.seed-train-ratio-Logistic-Regression-768x438.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/set.seed-train-ratio-Logistic-Regression-1024x584.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/set.seed-train-ratio-Logistic-Regression-520x296.png 520w\" sizes=\"auto, (max-width: 1298px) 100vw, 1298px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400\">Implementing our logistic regression function using the function\u00a0 &#8220;lm&#8221; and specifying the attribute family as \u201cbinomial\u201d, we obtain:<\/span><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">glm(Class ~ Cell.shape, family=\"binomial\", data = Train_Data)\r\n<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/glm-class-Logistic-Regression-train-data.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63671\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/glm-class-Logistic-Regression-train-data.png\" alt=\"glm-class-Logistic-Regression -train-data\" width=\"1296\" height=\"740\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/glm-class-Logistic-Regression-train-data.png 1296w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/glm-class-Logistic-Regression-train-data-150x86.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/glm-class-Logistic-Regression-train-data-300x171.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/glm-class-Logistic-Regression-train-data-768x439.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/glm-class-Logistic-Regression-train-data-1024x585.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/glm-class-Logistic-Regression-train-data-520x297.png 520w\" sizes=\"auto, (max-width: 1296px) 100vw, 1296px\" \/><\/a><\/p>\n<h2>Applications of Logistic Regression with R<\/h2>\n<ul>\n<li>It helps in image segmentation and categorisation.<br \/>\n<b><\/b><\/li>\n<li>Generally, we use logistic regression in geographic image processing.<br \/>\n<b><\/b><\/li>\n<li>It helps in handwriting recognition.<br \/>\n<b><\/b><\/li>\n<li>We use logistic regression in healthcare. That is an application area of logistic regression.<br \/>\n<b><\/b><\/li>\n<li>To make predictions about something that we use in logistic regression.<\/li>\n<\/ul>\n<h2>Summary<\/h2>\n<p>As a result, we have seen that logistic regression in R plays a very important role in R Programming. Therefore, with the help of this algorithm, we can conclude the important binary results.\u00a0 As we have discussed its syntax, parameters, derivations as well as examples. Also, we looked at the Logistic Regression Model in R with its performance.<\/p>\n<p><em><b>Now, it&#8217;s time to <a href=\"https:\/\/data-flair.training\/blogs\/r-decision-trees\/\">Create Decision Trees in R Programming<\/a><\/b><\/em><\/p>\n<p>Still, if you feel any confusion regarding R Logistic Regression, ask in the comment tab.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this tutorial, we will learn about the concept of logistic regression in R along with its syntax and parameters. We will also build a logistic regression model and explore its derivation, performance and&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":63664,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[51],"tags":[8389,20629,20631,20630],"class_list":["post-4938","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r","tag-logistic-regression-in-r","tag-r-logistic-regression-applications","tag-r-logistic-regression-model-building","tag-r-logistic-regression-model-performance"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Logistic Regression in R - A Detailed Guide for Beginners! - DataFlair<\/title>\n<meta name=\"description\" content=\"Want to learn about Logistic Regression in R? 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