{"id":25544915,"date":"2022-10-17T20:00:23","date_gmt":"2022-10-17T14:30:23","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25544915"},"modified":"2023-11-27T10:54:33","modified_gmt":"2023-11-27T05:24:33","slug":"what-is-logistic-regression-in-machine-learning","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/what-is-logistic-regression-in-machine-learning\/","title":{"rendered":"What is Logistic Regression in Machine Learning?"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e829fd18a5d\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e829fd18a5d\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/entri.app\/blog\/what-is-logistic-regression-in-machine-learning\/#Supervised_Learning\" >Supervised Learning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/entri.app\/blog\/what-is-logistic-regression-in-machine-learning\/#What_is_Logistic_Regression\" >What is Logistic Regression?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/entri.app\/blog\/what-is-logistic-regression-in-machine-learning\/#Advantages_of_Logistic_Regression_Algorithm\" >Advantages of Logistic Regression Algorithm<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/entri.app\/blog\/what-is-logistic-regression-in-machine-learning\/#How_Does_the_Logistic_Regression_Algorithm_Work\" >How Does the Logistic Regression Algorithm Work?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/entri.app\/blog\/what-is-logistic-regression-in-machine-learning\/#Applications_of_Logistic_Regression\" >Applications of Logistic Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/entri.app\/blog\/what-is-logistic-regression-in-machine-learning\/#The_assumption_in_a_Logistic_Regression_Algorithm\" >The assumption in a Logistic Regression Algorithm<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/entri.app\/blog\/what-is-logistic-regression-in-machine-learning\/#Predict_the_Digits_in_Images_Using_a_Logistic_Regression_Classifier_in_Python\" >Predict the Digits in Images Using a Logistic Regression Classifier in Python<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/entri.app\/blog\/what-is-logistic-regression-in-machine-learning\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<p>The business has undergone a revolution thanks to machine learning, enabling us to create complex apps that tackle challenging issues. Classification, regression, and clustering methods can be used to solve problems utilizing supervised and unsupervised machine learning models. In this post, we&#8217;ll talk about the Python implementation of the supervised machine learning technique known as logistic regression. Both classification and regression issues can be resolved using logistic regression.<\/p>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Learn Machine learning in advanced level. Join Entri now<\/a><\/strong><\/p>\n<h2 id=\"introduction_to_supervised_learning\"><span class=\"ez-toc-section\" id=\"Supervised_Learning\"><\/span><strong>Supervised Learning<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A labeled training dataset is used by supervised <a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">machine learning<\/a> algorithms to discover insights, patterns, and relationships. It indicates that each record in the collection already has a known value for the target variable. Because an algorithm learns from the training dataset under the supervision of an instructor, this process is known as supervised learning. The algorithm iteratively makes predictions based on the training data, and the instructor corrects it. You already know the right answers. Learning is complete when the algorithm reaches the desired performance and accuracy level<\/p>\n<p>Supervised learning problems can be further classified into regression and classification problems.<\/p>\n<ul>\n<li><strong>Classification<\/strong>: In a classification problem, the output variable is a category, such as \u201cred\u201d or \u201cblue,\u201d \u201cdisease\u201d or \u201cno disease,\u201d \u201ctrue\u201d or \u201cfalse,\u201d etc.<\/li>\n<li><strong>Regression<\/strong>: In a regression problem, the output variable is a real continuous value, such as \u201cdollars\u201d or \u201cweight.\u201d<\/li>\n<\/ul>\n<table>\n<tbody>\n<tr>\n<td style=\"text-align: center;\"><strong>Data Science Course in Different Cities<\/strong><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/entri.app\/course\/data-science-course-training-in-trivandrum\/\"><strong>Data Science Training Course in Trivandrum with Placement Assistance<\/strong><\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/entri.app\/course\/data-science-course-training-in-thrissur\/\"><strong>Data Science Training Course in Thrissur with Placement Assistance<\/strong><\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/entri.app\/course\/data-science-course-training-in-kochi\/\"><strong>Data Science Training Course in Kochi, Ernakulam with Placement Assistance<\/strong><\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/entri.app\/course\/data-science-course-training-in-calicut\/\"><strong>Data Science Training Course in Calicut with Placement Assistance<\/strong><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2 id=\"what_is_logistic_regression\"><span class=\"ez-toc-section\" id=\"What_is_Logistic_Regression\"><\/span><strong>What is Logistic Regression?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>When creating machine learning models, logistic regression is a statistical technique used when the dependent variable is dichotomous, or binary. Data and the relationship between one dependent variable and one or more independent variables are described using logistic regression. Nominal, ordinal, or interval types are all acceptable for the independent variables. The concept of the logistic function that it employs is where the term &#8220;logistic regression&#8221; originates. The sigmoid function is another name for the logistic function. This logistic function has a value between 0 and 1.<\/p>\n<h2 id=\"advantages_of_the_logistic_regression_algorithm\"><span class=\"ez-toc-section\" id=\"Advantages_of_Logistic_Regression_Algorithm\"><\/span><strong>Advantages of Logistic Regression Algorithm<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>Logistic regression performs better when the data is linearly separable<\/li>\n<li>It does not require too many computational resources as it\u2019s highly interpretable<\/li>\n<li>There is no problem scaling the input features\u2014It does not require tuning<\/li>\n<li>It is easy to implement and train a model using logistic regression<\/li>\n<li>It gives a measure of how relevant a predictor (coefficient size) is, and its direction of association (positive or negative)<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Learn Machine learning in advanced level. Join Entri now<\/a><\/strong><\/p>\n<h2 id=\"how_does_the_logistic_regression_algorithm_work\"><span class=\"ez-toc-section\" id=\"How_Does_the_Logistic_Regression_Algorithm_Work\"><\/span><strong>How Does the Logistic Regression Algorithm Work?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Think about the following instance: A company wants to decide whether to enhance an employee&#8217;s pay based on their performance. They will make their decision using a linear regression technique. They can simplify their task by plotting a regression line using the employee&#8217;s performance as the independent variable and the salary rise as the dependent variable. What happens if a company wants to determine whether a worker deserves a promotion based on their performance? The linear graph shown above won&#8217;t work in this situation. In order to create a sigmoid curve, we clip the line at zero and one (S curve). The company can decide whether or not to raise an employee&#8217;s wage based on the threshold values.<\/p>\n<p>To understand deeply, let\u2019s go over the odds of success.<\/p>\n<p>Odds (\ud835\udf03) = Probability of an event happening \/ Probability of an event not happening<\/p>\n<p>\ud835\udf03 = p \/ 1 &#8211; p<\/p>\n<p>The values of odds range from zero\u00a0 \u221e and the values of probability lie between zero and one.<\/p>\n<p>Consider the equation of a straight line:<\/p>\n<p>\ud835\udc66 = \ud835\udefd0 + \ud835\udefd1* \ud835\udc65<\/p>\n<p>Here, \ud835\udefd0 is the y-intercept<\/p>\n<p>\ud835\udefd1 is the slope of the line<\/p>\n<p>x is the value of the x coordinate<\/p>\n<p>y is the value of the prediction<\/p>\n<p>Now to predict the odds of success, we use the following formula:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"blend-mode\" src=\"https:\/\/www.simplilearn.com\/ice9\/free_resources_article_thumb\/log.JPG\" alt=\"log\" width=\"229\" height=\"54\" \/><\/p>\n<p>Exponentiating both sides, we have:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"blend-mode\" src=\"https:\/\/www.simplilearn.com\/ice9\/free_resources_article_thumb\/let-y.JPG\" alt=\"et-y\" width=\"282\" height=\"180\" \/><\/p>\n<p>Let Y = e \ud835\udefd0+\ud835\udefd1 * \ud835\udc65<\/p>\n<p>Then p(x) \/ 1 &#8211; p(x) = Y<\/p>\n<p>p(x) = Y(1 &#8211; p(x))<\/p>\n<p>p(x) = Y &#8211; Y(p(x))<\/p>\n<p>p(x) + Y(p(x)) = Y<\/p>\n<p>p(x)(1+Y) = Y<\/p>\n<p>p(x) = Y \/ 1+Y<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"blend-mode\" src=\"https:\/\/www.simplilearn.com\/ice9\/free_resources_article_thumb\/px.JPG\" alt=\"px\" width=\"229\" height=\"82\" \/><\/p>\n<p>The equation of the sigmoid function is:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"blend-mode\" src=\"https:\/\/www.simplilearn.com\/ice9\/free_resources_article_thumb\/px-2.JPG\" alt=\"\" width=\"241\" height=\"80\" \/><\/p>\n<p>The sigmoid curve obtained from the above equation is as follows:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"blend-mode\" src=\"https:\/\/www.simplilearn.com\/ice9\/free_resources_article_thumb\/sigmoid.JPG\" alt=\"\" width=\"293\" height=\"217\" \/><\/p>\n<p><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0<strong> Join for Data science &amp; Machine Learning Course<\/strong><\/a><\/p>\n<h2 id=\"applications_of_logistic_regression\"><span class=\"ez-toc-section\" id=\"Applications_of_Logistic_Regression\"><\/span><strong>Applications of Logistic Regression<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>Using the logistic regression algorithm, banks can predict whether a customer would default on loans or not<\/li>\n<li>To predict the weather conditions of a certain place (sunny, windy, rainy, humid, etc.)<\/li>\n<li>Ecommerce companies can identify buyers if they are likely to purchase a certain product<\/li>\n<li>Companies can predict whether they will gain or lose money in the next quarter, year, or month based on their current performance<\/li>\n<li>To classify objects based on their features and attributes<\/li>\n<\/ul>\n<table>\n<tbody>\n<tr>\n<td style=\"text-align: center;\" colspan=\"3\">\n<h5><span style=\"color: #ffffff;\"><strong>Are you aspiring for a booming career in IT? If YES, then dive in<\/strong><\/span><\/h5>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<h5><a href=\"https:\/\/entri.app\/course\/full-stack-developer-course\/\"><strong>Full Stack Developer Course<\/strong><\/a><\/h5>\n<\/td>\n<td>\n<h5><a href=\"https:\/\/entri.app\/course\/python-programming-course\/\"><strong>Python Programming Course<\/strong><\/a><\/h5>\n<\/td>\n<td>\n<h5><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\"><strong>Data Science and Machine Learning Course<\/strong><\/a><\/h5>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2 id=\"assumption_in_a_logistic_regression_algorithm\"><span class=\"ez-toc-section\" id=\"The_assumption_in_a_Logistic_Regression_Algorithm\"><\/span><strong>The assumption in a Logistic Regression Algorithm<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>In a binary logistic regression, the dependent variable must be binary<\/li>\n<li>For a binary regression, the factor level one of the dependent variables should represent the desired outcome<\/li>\n<li>Only meaningful variables should be included<\/li>\n<li>The independent variables should be independent of each other. This means the model should have little or no multicollinearity<\/li>\n<li>The independent variables are linearly related to the log odds<\/li>\n<li>Logistic regression requires quite large sample sizes<\/li>\n<\/ul>\n<p><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 <strong>\u00a0Join for Data science &amp; Machine Learning Course<\/strong><\/a><\/p>\n<h2 id=\"use_case_predict_the_digits_in_images_using_a_logistic_regression_classifier_in_python\"><span class=\"ez-toc-section\" id=\"Predict_the_Digits_in_Images_Using_a_Logistic_Regression_Classifier_in_Python\"><\/span><strong>Predict the Digits in Images Using a Logistic Regression Classifier in Python<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>We\u2019ll be using the digits dataset in the sci-kit learn library to predict digit values from images using the logistic regression model in Python.<\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li>Importing libraries and their associated methods<\/li>\n<li>Determining the total number of images and labels<\/li>\n<li>Displaying some of the images and their labels<\/li>\n<li>Dividing the dataset into \u201ctraining\u201d and \u201ctest\u201d set<\/li>\n<li>Importing the logistic regression model<\/li>\n<li>Making an instance of the model and training it<\/li>\n<li>Predicting the output of the first element of the test set<\/li>\n<li>Predicting the output of the first 10 elements of the test set<\/li>\n<li>Prediction for the entire dataset<\/li>\n<li>Determining the accuracy of the model<\/li>\n<li>Representing the confusion matrix in a heat map<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"blend-mode\" src=\"https:\/\/www.simplilearn.com\/ice9\/free_resources_article_thumb\/actual-output.JPG\" alt=\"actual-output\" width=\"599\" height=\"300\" \/><\/p>\n<ul>\n<li>Presenting predictions and actual output<\/li>\n<\/ul>\n<p>The images above depict the actual numbers and the predicted digit values from our logistic regression model.<\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>We anticipated that this essay would help you become familiar with the fundamentals of logistic regression and supervised learning. We discussed the logistic regression algorithm in depth using a complex case. The list of assumptions you should make to develop a logistic regression model was then discussed, after which we looked at the various applications of logistic regression.<\/p>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">Get the latest updates on machine learning in the Entri app<\/a><\/strong><\/p>\n<h4><strong>Related Articles<\/strong><\/h4>\n<div class=\"table-responsive wprt_style_display\">\n<table class=\"table\" dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<colgroup>\n<col width=\"329\" \/>\n<col width=\"309\" \/><\/colgroup>\n<tbody>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Syllabus&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-village-field-assistant-vfa-syllabus-exam-pattern\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/what-is-regularization-in-machine-learning\/\" target=\"_blank\" rel=\"noopener\">What is Regularization in Machine Learning?<\/a><\/strong><\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Mock Test&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-free-mock-test\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/importance-of-data-preprocessing-in-machine-learning\/\" target=\"_blank\" rel=\"noopener\">Importance of Data Preprocessing in Machine Learning<\/a><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Exam Date&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-exam-date\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/what-is-data-mining-a-complete-guide\/\" target=\"_blank\" rel=\"noopener\">What Is Data Mining?<\/a><\/strong><\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Video Course&quot;}\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/top-data-types-of-python-python-data-types\/\" target=\"_blank\" rel=\"noopener\">Top Data Types of Python<\/a><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Application Form&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-apply-online\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/what-is-data-interpretation-methods-and-benefits\/\" target=\"_blank\" rel=\"noopener\">What is Data Interpretation? 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Classification, regression, and clustering methods can be used to solve problems utilizing supervised and unsupervised machine learning models. In this post, we&#8217;ll talk about the Python implementation of the supervised machine learning technique known as [&hellip;]<\/p>\n","protected":false},"author":93,"featured_media":25544923,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1903,1864,1882,1883,1881],"tags":[],"class_list":["post-25544915","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-coding","category-data-science-ml","category-java-programming","category-react-native","category-web-android-development"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What is Logistic Regression in Machine Learning? - Entri Blog<\/title>\n<meta name=\"description\" content=\"This article shows Python implementation of the supervised machine learning technique known as logistic regression. 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