{"id":25544757,"date":"2022-10-16T16:34:43","date_gmt":"2022-10-16T11:04:43","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25544757"},"modified":"2023-11-23T12:52:37","modified_gmt":"2023-11-23T07:22:37","slug":"classification-vs-regression-in-machine-learning","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/classification-vs-regression-in-machine-learning\/","title":{"rendered":"Classification vs 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-69e6f08f9aca1\" 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-69e6f08f9aca1\"  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\/classification-vs-regression-in-machine-learning\/#Regression_vs_Classification_in_Machine_Learning_How_they_Differ\" >Regression vs Classification in Machine Learning: How they Differ<\/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\/classification-vs-regression-in-machine-learning\/#Regression_vs_Classification_in_Machine_Learning_Understanding_the_Difference\" >Regression vs Classification in Machine Learning: Understanding the Difference<\/a><\/li><\/ul><\/nav><\/div>\n<p>Comparing regression vs classification in machine learning can sometimes confuse even the most seasoned data scientists. This can eventually make it difficult for them to implement the right methodologies for solving prediction problems. Both regression and classification are types of supervised machine learning algorithms, where a model is trained according to the existing model along with correctly labeled data. But there are also many differences between regression and classification algorithms that you should know in order to implement them correctly and sharpen your machine learning skills. In this blog, we will understand the difference between regression and classification algorithms.<\/p>\n<p><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-25520910 size-full\" src=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png\" alt=\"Python and Machine Learning Square\" width=\"345\" height=\"345\" srcset=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png 345w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-300x300.png 300w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-150x150.png 150w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-24x24.png 24w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-48x48.png 48w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-96x96.png 96w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-75x75.png 75w\" sizes=\"auto, (max-width: 345px) 100vw, 345px\" \/><\/a><\/p>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">Get a free demo for\u00a0 our certificate program in data science and Machine learning<\/a><\/strong><\/p>\n<h2 id=\"h0\"><span class=\"ez-toc-section\" id=\"Regression_vs_Classification_in_Machine_Learning_How_they_Differ\"><\/span><strong>Regression vs Classification in Machine Learning: How they Differ<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Some algorithms may need both classification and regression approaches, which is why an in-depth knowledge of both is crucial in the fields of AI and\u00a0data science. Before we deep dive into understanding the differences between regression and classification algorithms. Let\u2019s first understand each algorithm.<\/p>\n<h3><strong>What is Regression Machine Learning?<\/strong><\/h3>\n<p>Regression algorithms predict a continuous value based on the input variables. The main goal of regression problems is to estimate a mapping function based on the input and output variables. If your target variable is a quantity like income, scores, height or weight, or the probability of a binary category (like the probability of rain in particular regions), then you should use the regression model. However, there are various types of regressions used by\u00a0data scientists\u00a0and ML engineers based on different scenarios. The different types of regression algorithms include:<\/p>\n<h4><strong>1. Simple linear regression<\/strong><\/h4>\n<p>With simple linear regression, you can estimate the relationship between one independent variable and another dependent variable using a straight line, given both variables are quantitative.<\/p>\n<h4><strong>2. Multiple linear regression<\/strong><\/h4>\n<p>An extension of simple linear regression, multiple regression can predict the values of a dependent variable based on the values of two or more independent variables.<\/p>\n<h4><strong>3. Polynomial regression<\/strong><\/h4>\n<p>The main aim of polynomial regression is to model or find a nonlinear relationship between dependent and independent variables.<\/p>\n<h4 style=\"text-align: center;\"><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">Learn data science and machine learning course<\/a><\/h4>\n<h3><strong>What is Classification Machine Learning?<\/strong><\/h3>\n<p>Classification is a predictive model that approximates a mapping function from input variables to identify discrete output variables, which can be labels or categories. The mapping function of classification algorithms is responsible for predicting the label or category of the given input variables.\u00a0A classification algorithm can have both discrete and real-valued variables, but it requires that the examples be classified into one of two or more classes.<\/p>\n<p style=\"text-align: center;\"><a class=\"btn btn-default\" href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">BE A DATA SCIENTIST WITH ONLINE COURSE ! ENROLL NOW !!<\/a><\/p>\n<p>The different types of classification algorithms include:<\/p>\n<h4><strong>1. Decision tree classification<\/strong><\/h4>\n<p>In this algorithm, a classification model is created by building a decision tree where every node of the tree is a test case for an attribute and each branch coming from the node is a possible value for that attribute.<\/p>\n<h4><strong>2. Random forest classification<\/strong><\/h4>\n<p>This tree-based algorithm includes a set of decision trees which are randomly selected from a subset of the main training set. The random forest classification algorithm aggregates outputs from all the different decision trees to decide on the final output prediction, which is more accurate than any of the individual trees.<\/p>\n<h4><strong>3. K-nearest neighbor<\/strong><\/h4>\n<p>The K-nearest neighbor algorithm assumes that similar things exist in close proximity to each other. It uses feature similarity for predicting values of new data points. The algorithm helps grouping similar data points together according to their proximity. The main goal of the algorithm is to determine how likely it is for a data point to be a part of the specific group.<\/p>\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=\"h1\"><span class=\"ez-toc-section\" id=\"Regression_vs_Classification_in_Machine_Learning_Understanding_the_Difference\"><\/span><strong>Regression vs Classification in Machine Learning: Understanding the Difference<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The most significant difference between regression vs classification is that while regression helps predict a continuous quantity, classification predicts discrete class labels. There are also some overlaps between the two types of machine learning algorithms.<\/p>\n<ul>\n<li>A regression algorithm can predict a discrete value which is in the form of an integer quantity<\/li>\n<li>A classification algorithm can predict a continuous value if it is in the form of a class label probability<\/li>\n<\/ul>\n<p>Let\u2019s consider a dataset that contains student information of a particular university. A regression algorithm can be used in this case to predict the height of any student based on their weight, gender, diet, or subject major. We use regression in this case because height is a continuous quantity. There is an infinite number of possible values for a person\u2019s height.<\/p>\n<p><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-25522670 size-full\" src=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1.png\" alt=\"Python and Machine Learning Rectangle\" width=\"970\" height=\"250\" srcset=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1.png 970w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1-300x77.png 300w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1-768x198.png 768w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1-750x193.png 750w\" sizes=\"auto, (max-width: 970px) 100vw, 970px\" \/><\/a><\/p>\n<p>On the contrary, classification can be used to analyze whether an email is a spam or not spam. The algorithm checks the keywords in an email and the sender\u2019s address is to find out the probability of the email being spam. Similarly, while a regression model can be used to predict temperature for the next day, we can use a classification algorithm to determine whether it will be cold or hot according to the given temperature values.<\/p>\n<h4 style=\"text-align: center;\"><a href=\"https:\/\/entri.app\/course\/full-stack-developer-course\/\" target=\"_blank\" rel=\"noopener\"><strong>Start your Data Science and Machine Learning classes now! 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This can eventually make it difficult for them to implement the right methodologies for solving prediction problems. Both regression and classification are types of supervised machine learning algorithms, where a model is trained according to the existing model along [&hellip;]<\/p>\n","protected":false},"author":111,"featured_media":25544881,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1903,1864],"tags":[],"class_list":["post-25544757","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-coding","category-data-science-ml"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Classification vs Regression in Machine Learning - Entri Blog<\/title>\n<meta name=\"description\" content=\"In this blog, you will understand the difference between regression and classification algorithms.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/entri.app\/blog\/classification-vs-regression-in-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Classification vs Regression in Machine Learning - 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