{"id":4457,"date":"2023-08-07T04:11:12","date_gmt":"2023-08-07T04:11:12","guid":{"rendered":"https:\/\/pickl.ai\/blog\/?p=4457"},"modified":"2024-08-02T09:26:01","modified_gmt":"2024-08-02T09:26:01","slug":"regression-in-machine-learning-types-examples","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/","title":{"rendered":"Regression in Machine Learning: Types &#038; Examples"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Summary: This blog explores regression in machine learning, detailing various types, such as linear, polynomial, and ridge regression. It explains when to use each model and their applications for predicting continuous outcomes.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><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><\/span><\/a><\/span><\/div>\n<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:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#What_is_Regression_in_ML\" >What is Regression in ML?\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#15_Types_of_Regression_Models_when_to_use_them\" >15 Types of Regression Models &amp; when to use them<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Linear_Regression\" >Linear Regression\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Multiple_Linear_Regression\" >Multiple Linear Regression\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Polynomial_Regression\" >Polynomial Regression\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Ridge_Regression_L2_Regularisation\" >Ridge Regression (L2 Regularisation)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Lasso_Regression_L1_Regularisation\" >Lasso Regression (L1 Regularisation)\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#6_Decision_Tree_Regression\" >6. Decision Tree Regression:\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Logistic_Regression\" >Logistic Regression\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Poisson_Regression\" >Poisson Regression\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Negative_Binomial_Regression\" >Negative Binomial Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Cox_Regression_Proportional_Hazards_Model\" >Cox Regression (Proportional Hazards Model)\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Stepwise_Regression\" >Stepwise Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Time_Series_Regression\" >Time Series Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Panel_Data_Regression_Fixed_Effects_and_Random_Effects_Models\" >Panel Data Regression (Fixed Effects and Random Effects Models)\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Bayesian_Regression\" >Bayesian Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Quantile_Regression\" >Quantile Regression<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#What_is_a_regression_in_machine_learning\" >What is a regression in machine learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#When_should_I_use_polynomial_regression\" >When should I use polynomial regression?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#What_is_the_difference_between_ridge_and_lasso_regression\" >What is the difference between ridge and lasso regression?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><b>Introduction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><a href=\"https:\/\/pickl.ai\/blog\/what-is-machine-learning\/\"><span style=\"font-weight: 400;\">Machine Learning<\/span><\/a><span style=\"font-weight: 400;\"> has become a fundamental part of people\u2019s lives, and it typically has two segments: <\/span><a href=\"https:\/\/pickl.ai\/blog\/supervised-learning-vs-unsupervised-learning\/\"><span style=\"font-weight: 400;\">supervised and unsupervised learning<\/span><\/a><span style=\"font-weight: 400;\">. Supervised Learning deals with labelled data, and unsupervised learning deals with unlabelled data.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Supervised learning can be classified into classification and regression, where regression deals with continuous values and the former deals with discrete values. The following blog revolves around Regression in Machine Learning and its types.\u00a0<\/span><\/p>\n<h2 id=\"what-is-regression-in-ml\"><span class=\"ez-toc-section\" id=\"What_is_Regression_in_ML\"><\/span><b>What is Regression in ML?\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Regression <\/span><a href=\"https:\/\/pickl.ai\/blog\/machine-learning-algorithms-that-every-ml-engineer-should-know\/\"><span style=\"font-weight: 400;\">Machine Learning algorithms<\/span><\/a><span style=\"font-weight: 400;\"> are a statistical method for modelling the relationship between dependent variables and one or more independent variables. The analysis helps you understand the change in the value of the target variable corresponding to an independent variable.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It is possible when the other independent variables are held at a fixed value. There are different types of regression in Machine Learning Regression algorithms, where the target variable with continuous values and independent variables show a linear or non-linear relationship.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Effectively, regression algorithms help determine the best-fit line. It passes through all data points in a way that the distance of the line from each data point is minimal.<\/span><\/p>\n<h2 id=\"15-types-of-regression-models-when-to-use-them\"><span class=\"ez-toc-section\" id=\"15_Types_of_Regression_Models_when_to_use_them\"><\/span><b>15 Types of Regression Models &amp; when to use them<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Regression algorithm models are statistical techniques used to model the relationship between one or more independent variables (predictors) and a dependent variable (response). There are various regression <\/span><a href=\"https:\/\/pickl.ai\/blog\/tag\/types-of-machine-learning-model\/\"><span style=\"font-weight: 400;\">models in ML<\/span><\/a><span style=\"font-weight: 400;\">, each designed for specific scenarios and data types. Here are 15 types of regression models and when to use them:<\/span><\/p>\n<h3 id=\"linear-regression\"><span class=\"ez-toc-section\" id=\"Linear_Regression\"><\/span><b>Linear Regression\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Linear regression is used when the relationship between the dependent and independent variables is assumed to be linear. It is suitable for continuous numerical data and when a straight line can predict the response variable.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Linear regression is a fundamental and widely used statistical method for modelling the relationship between a dependent variable (Y) and one or more independent variables (X).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It assumes a linear relationship between the predictor(s) and the response variable. Mathematically, a simple linear regression can be expressed as Y = \u03b20 + \u03b21*X + \u03b5, where \u03b20 and \u03b21 are the coefficients and \u03b5 represents the error term.<\/span><\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-12995\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2.png\" alt=\"Linear Regression \" width=\"1400\" height=\"775\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2.png 1400w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-300x166.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-1024x567.png 1024w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-768x425.png 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-110x61.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-200x111.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-380x210.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-255x141.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-550x304.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-800x443.png 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-1160x642.png 1160w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image7-2-150x83.png 150w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> Suppose we want to predict house prices (Y) based on their size (X). We collect data on various houses, their respective sizes, and their actual selling prices. The goal is to fit a straight line that best describes the relationship between house size and price.<\/span><\/p>\n<h3 id=\"multiple-linear-regression\"><span class=\"ez-toc-section\" id=\"Multiple_Linear_Regression\"><\/span><b>Multiple Linear Regression\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">It is similar to linear regression, but it involves multiple independent variables. It is used when the response variable depends on more than one predictor variable.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Multiple linear regression extends the concept of simple linear regression to include more than one independent variable. The model becomes Y = \u03b20 + \u03b21X1 + \u03b22X2 + \u2026 + \u03b2n*Xn + \u03b5, where n is the number of predictors.<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-12996\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image10-1.png\" alt=\"Multiple Linear Regression\" width=\"709\" height=\"599\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image10-1.png 709w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image10-1-300x253.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image10-1-110x93.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image10-1-200x169.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image10-1-380x321.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image10-1-255x215.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image10-1-550x465.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image10-1-150x127.png 150w\" sizes=\"(max-width: 709px) 100vw, 709px\" \/><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> Building on the house price prediction example, we can include additional features such as the number of bedrooms, location, and house age. The multiple linear regression model will help us understand how each predictor contributes to the overall price prediction.<\/span><\/p>\n<h3 id=\"polynomial-regression\"><span class=\"ez-toc-section\" id=\"Polynomial_Regression\"><\/span><b>Polynomial Regression\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Polynomial regression is used when a polynomial function rather than a straight line can better approximate the relationship between the dependent and independent variables. It is suitable when data follows a curvilinear pattern.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sometimes, the relationship between the predictors and the response variable may not be linear. Polynomial regression allows us to capture more complex patterns by using polynomial functions of the predictors (X). The model can be expressed as: Y = \u03b20 + \u03b21X + \u03b22X^2 + \u2026 + \u03b2n*X^n + \u03b5.<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-12998\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1.png\" alt=\"Polynomial Regression\" width=\"1500\" height=\"902\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1.png 1500w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-300x180.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-1024x616.png 1024w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-768x462.png 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-110x66.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-200x120.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-380x229.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-255x153.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-550x331.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-800x481.png 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-1160x698.png 1160w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image8-1-150x90.png 150w\" sizes=\"(max-width: 1500px) 100vw, 1500px\" \/><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> Consider the temperature and gas consumption example. If gas consumption increases with temperature nonlinearly, polynomial regression can help us model this relationship more accurately.<\/span><\/p>\n<h3 id=\"ridge-regression-l2-regularisation\"><span class=\"ez-toc-section\" id=\"Ridge_Regression_L2_Regularisation\"><\/span><b>Ridge Regression (L2 Regularisation)<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In multiple linear regression, ridge regression is used to handle multicollinearity (high correlation between predictors). It stabilises the model by adding a penalty term to the least squares objective function.<\/span><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/l1-and-l2-regularization-in-machine-learning\/\"><span style=\"font-weight: 400;\">Ridge regression<\/span><\/a><span style=\"font-weight: 400;\"> is a regularised linear regression that addresses multicollinearity issues (high correlation between predictors). It adds a penalty term (L2 norm) to the least squares objective function, which prevents large coefficient values. This regularisation helps to stabilise the model and reduces overfitting.<\/span><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> In a sales prediction scenario, advertising expenses and promotion budgets might be highly correlated. Ridge regression can be used to prevent overemphasising one of these variables and achieve a more robust model.<\/span><\/p>\n<h3 id=\"lasso-regression-l1-regularisation\"><span class=\"ez-toc-section\" id=\"Lasso_Regression_L1_Regularisation\"><\/span><b>Lasso Regression (L1 Regularisation)\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/pickl.ai\/blog\/lasso-regression\/\"><span style=\"font-weight: 400;\">Lasso regression<\/span><\/a><span style=\"font-weight: 400;\"> is used when you want to perform feature selection along with regression. It adds an absolute value penalty term to the least squares objective function, forcing some coefficients to become precisely zero.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Lasso regression is another regularisation technique used for feature selection along with regression. It adds an absolute value penalty term (L1 norm) to the least squares objective function. This causes some coefficients to become exactly zero, effectively performing variable selection.<\/span><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> In a medical study, several potential predictors of disease occurrence exist. Lasso regression can help identify the most relevant predictors and eliminate the less important ones.<\/span><\/p>\n<h3 id=\"6-decision-tree-regression\"><span class=\"ez-toc-section\" id=\"6_Decision_Tree_Regression\"><\/span><b>6. Decision Tree Regression:\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Decision tree regression is a non-parametric Machine Learning technique for predicting continuous values. It constructs a tree-like structure by recursively splitting the data based on feature values, creating branches and leaf nodes.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Each leaf node represents a predicted value for the target variable. The algorithm is simple to interpret and can capture complex relationships in the data.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-13001\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13.jpg\" alt=\"Decision Tree Regression: \" width=\"1000\" height=\"667\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13-300x200.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13-768x512.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13-110x73.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13-200x133.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13-380x253.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13-255x170.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13-550x367.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13-800x534.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-13-150x100.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> Suppose we have a dataset containing information about houses, including their size, number of bedrooms, and sale prices. We want to use decision tree regression to predict the cost of a new home based on its features.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The <\/span><a href=\"https:\/\/pickl.ai\/blog\/decision-tree-classification-a-guide-to-machine-learning-algorithm\/\"><span style=\"font-weight: 400;\">decision tree algorithm<\/span><\/a><span style=\"font-weight: 400;\"> analyses the data and creates a tree structure. First, the data might be split based on the size of the house. If the home is smaller than a certain threshold, it goes to the left branch; if it\u2019s larger, it goes to the right branch. Then, the data is split further based on the number of bedrooms.<\/span><\/p>\n<p><b>Read Blog:<\/b> <a href=\"https:\/\/pickl.ai\/blog\/a-tale-of-regression-and-regressiveness\/\"><span style=\"font-weight: 400;\">A tale of regression and regressiveness<\/span><\/a><\/p>\n<h3 id=\"logistic-regression\"><span class=\"ez-toc-section\" id=\"Logistic_Regression\"><\/span><b>Logistic Regression\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Logistic regression is a powerful statistical method designed for binary classification tasks, where the goal is to predict one of two possible outcomes, such as yes\/no or true\/false. This technique models the probability of a specific outcome occurring by applying a logistic (or sigmoid) function to a linear combination of predictor variables.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By transforming these predictors through the logistic function, the model outputs a probability that ranges between 0 and 1. This allows it to estimate the likelihood of the binary response, making logistic regression a crucial tool for decision-making in scenarios where outcomes are categorical and mutually exclusive.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-13003\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-7.png\" alt=\"\" width=\"460\" height=\"377\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-7.png 460w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-7-300x246.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-7-110x90.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-7-200x164.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-7-380x311.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-7-255x209.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-7-150x123.png 150w\" sizes=\"(max-width: 460px) 100vw, 460px\" \/><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> In email spam classification, <\/span><a href=\"https:\/\/pickl.ai\/blog\/what-is-logistic-regression\/\"><span style=\"font-weight: 400;\">logistic regression<\/span><\/a><span style=\"font-weight: 400;\"> can predict the probability that an email is spam based on various email features.<\/span><\/p>\n<h3 id=\"poisson-regression\"><span class=\"ez-toc-section\" id=\"Poisson_Regression\"><\/span><b>Poisson Regression\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Poisson regression is a statistical technique design for modelling count data, where the dependent variable represents the number of occurrences of an event within a fixed period. This method is particularly useful when the data follows a Poisson distribution, which describes the probability of a given number of events happening within a fixed interval.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Unlike other regression models, Poisson regression assumes that the mean rate of occurrence is equal to the variance, making it suitable for data with low to moderate event counts. Analysts can effectively predict and interpret the relationship between count data and independent variables by applying Poisson regression, offering insights into event patterns and rates.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-13004\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image9-1.png\" alt=\"Poisson Regression\" width=\"709\" height=\"599\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image9-1.png 709w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image9-1-300x253.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image9-1-110x93.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image9-1-200x169.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image9-1-380x321.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image9-1-255x215.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image9-1-550x465.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image9-1-150x127.png 150w\" sizes=\"(max-width: 709px) 100vw, 709px\" \/><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> Modeling the number of customer service calls a company receives daily based on factors like day of the week, advertising campaigns, or seasonal effects.<\/span><\/p>\n<h3 id=\"negative-binomial-regression\"><span class=\"ez-toc-section\" id=\"Negative_Binomial_Regression\"><\/span><b>Negative Binomial Regression<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Negative binomial regression builds on Poisson regression to address overdispersion in count data. While Poisson regression assumes that the variance equals the mean, negative binomial regression allows for greater flexibility by introducing an additional parameter to account for overdispersion. This extra parameter captures the variability in the data that Poisson regression cannot handle.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As a result, negative binomial regression provides a more accurate model for datasets where the variance exceeds the mean, making it especially useful for analysing count data with high variability. Accommodating this extra dispersion offers a more robust and reliable analysis of count outcomes.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-13005\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3.png\" alt=\"Negative Binomial Regression\" width=\"1600\" height=\"1161\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3.png 1600w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-300x218.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-1024x743.png 1024w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-768x557.png 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-1536x1115.png 1536w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-110x80.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-200x145.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-380x276.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-255x185.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-550x399.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-800x581.png 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-1160x842.png 1160w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image5-3-150x109.png 150w\" sizes=\"(max-width: 1600px) 100vw, 1600px\" \/><\/p>\n<p><b>Example: <\/b><span style=\"font-weight: 400;\">Predicting the number of accidents in a factory per day, where the count data might show more variation than expected from a simple Poisson model.<\/span><\/p>\n<h3 id=\"cox-regression-proportional-hazards-model\"><span class=\"ez-toc-section\" id=\"Cox_Regression_Proportional_Hazards_Model\"><\/span><b>Cox Regression (Proportional Hazards Model)\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Cox regression, also known as the Proportional Hazards Model, is a key statistical method in survival analysis. It examines the impact of predictor variables on the time until an event occurs, such as patient survival time in medical studies.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This model is precious in clinical research, where it helps to identify how different factors, like treatments or demographics, influence the risk of an event over time.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By estimating hazard ratios, Cox regression allows researchers to understand and quantify the relationship between covariates and the hazard of experiencing the event, providing critical insights for decision-making and prognosis.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-13006\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image11.png\" alt=\"Cox Regression (Proportional Hazards Model)\" width=\"709\" height=\"599\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image11.png 709w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image11-300x253.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image11-110x93.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image11-200x169.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image11-380x321.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image11-255x215.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image11-550x465.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image11-150x127.png 150w\" sizes=\"(max-width: 709px) 100vw, 709px\" \/><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> Analysing the impact of different treatments on the survival time of cancer patients after diagnosis.<\/span><\/p>\n<h3 id=\"stepwise-regression\"><span class=\"ez-toc-section\" id=\"Stepwise_Regression\"><\/span><b>Stepwise Regression<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/www.investopedia.com\/terms\/s\/stepwise-regression.asp\"><span style=\"font-weight: 400;\">Stepwise regression<\/span><\/a><span style=\"font-weight: 400;\"> is a statistical technique to build efficient and simplified models by selecting the most significant predictor variables from a more extensive set. This method systematically adds or removes variables based on their contribution to the model\u2019s predictive power. Initially, it starts with no variables in the model or a subset of predictors.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It then evaluates the impact of adding each variable, selecting those that improve the model\u2019s performance. Conversely, it may also remove variables that no longer contribute meaningfully. This iterative process helps create a model that balances complexity and accuracy, including only the most relevant predictors.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-13008\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image12.png\" alt=\"\" width=\"709\" height=\"599\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image12.png 709w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image12-300x253.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image12-110x93.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image12-200x169.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image12-380x321.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image12-255x215.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image12-550x465.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image12-150x127.png 150w\" sizes=\"(max-width: 709px) 100vw, 709px\" \/><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> Selecting the most important features from a large dataset to predict the market performance of a particular product.<\/span><\/p>\n<h3 id=\"time-series-regression\"><span class=\"ez-toc-section\" id=\"Time_Series_Regression\"><\/span><b>Time Series Regression<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Time series regression is employe when analysing data points collected or recorded at successive intervals. This method focuses on understanding how the dependent variable, a time series, is influenced by its previous values or lagged observations.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In other words, it examines how past values of the dependent variable and different independent variables impact its future values. By incorporating lagged variables into the model, time series regression helps capture patterns and trends over time, allowing for better predictions and insights into temporal dynamics within the data.<\/span><\/p>\n<p><b>Example: <\/b><span style=\"font-weight: 400;\">Predicting a company&#8217;s stock prices based on its past stock prices and economic indicators.<\/span><\/p>\n<p><b>Explore:<\/b> <a href=\"https:\/\/pickl.ai\/blog\/time-series-database\/\"><span style=\"font-weight: 400;\">Demystifying Time Series Database: A Comprehensive Guide<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3 id=\"panel-data-regression-fixed-effects-and-random-effects-models\"><span class=\"ez-toc-section\" id=\"Panel_Data_Regression_Fixed_Effects_and_Random_Effects_Models\"><\/span><b>Panel Data Regression (Fixed Effects and Random Effects Models)\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Panel data regression is a powerful analytical technique used when researchers collect data from multiple entities over time. This method allows for controlling individual-specific effects, which are constant over time but vary between entities, through fixed-effects models.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By focusing on within-entity variations, fixed-effects models help isolate and analyse the influence of variables unique to each entity. Alternatively, random effects models account for random variations across entities and assume these effects are uncorrelated with the explanatory variables.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This approach is beneficial when the variability is more general and less specific to each entity. Together, these models provide robust insights into temporal and entity-specific dynamics.<\/span><\/p>\n<p><b>Example: <\/b><span style=\"font-weight: 400;\">Analysing the impact of educational policies on students\u2019 test scores across different schools over several years.<\/span><\/p>\n<h3 id=\"bayesian-regression\"><span class=\"ez-toc-section\" id=\"Bayesian_Regression\"><\/span><b>Bayesian Regression<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/en.wikipedia.org\/wiki\/Bayesian_linear_regression\"><span style=\"font-weight: 400;\">Bayesian regression<\/span><\/a><span style=\"font-weight: 400;\"> is a powerful technique incorporating prior knowledge or beliefs about model parameters into the regression analysis. Unlike traditional regression methods that yield point estimates, Bayesian regression offers a probabilistic framework, allowing you to understand the uncertainty around predictions.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This approach combines prior distributions, representing your initial beliefs about the parameters, with the likelihood of the observed data.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As a result, Bayesian regression provides a range of possible values for the parameters rather than a single estimate, which helps make more informed decisions under uncertainty. This method is beneficial when dealing with complex models or limited data.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-13010\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-4.png\" alt=\"\" width=\"616\" height=\"461\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-4.png 616w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-4-300x225.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-4-110x82.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-4-200x150.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-4-380x284.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-4-255x191.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-4-550x412.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-4-150x112.png 150w\" sizes=\"(max-width: 616px) 100vw, 616px\" \/><\/p>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> Estimating the demand for a product based on past sales data while incorporating prior knowledge about similar products and market trends.<\/span><\/p>\n<h3 id=\"quantile-regression\"><span class=\"ez-toc-section\" id=\"Quantile_Regression\"><\/span><b>Quantile Regression<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Quantile regression is a statistical technique that models the relationship between predictors and various quantiles of the dependent variable. Unlike ordinary least squares regression, which focuses solely on the mean of the response variable, quantile regression provides insights into different points of the conditional distribution.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Analysing different quantiles, such as the median or the 90th percentile, offers a richer understanding of how predictors influence the entire distribution of the outcome variable. This method is handy when the data exhibits heteroscedasticity or when outliers might disproportionately affect the mean, allowing for a more nuanced view of the data&#8217;s variability.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-13011\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-5.png\" alt=\"Quantile Regression\" width=\"709\" height=\"599\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-5.png 709w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-5-300x253.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-5-110x93.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-5-200x169.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-5-380x321.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-5-255x215.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-5-550x465.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-5-150x127.png 150w\" sizes=\"(max-width: 709px) 100vw, 709px\" \/><\/p>\n<p><b>Example: <\/b><span style=\"font-weight: 400;\">Studying the relationship between weather variables and electricity consumption at various quantiles to understand the impact on different demand levels.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The choice of regression model depends on the nature of your data, the assumptions of the relationship between variables, the type of dependent variable, and your specific research or prediction objectives. Always validate the chosen model\u2019s assumptions and assess its performance using appropriate evaluation metrics before concluding the results.<\/span><\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"what-is-a-regression-in-machine-learning\"><span class=\"ez-toc-section\" id=\"What_is_a_regression_in_machine_learning\"><\/span><b>What is a regression in machine learning?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Regression in machine learning is a statistical method for modelling the relationship between a dependent variable and one or more independent variables. It aims to predict continuous outcomes by finding the best-fit line or curve through data points, minimising the distance between the line and actual data.<\/span><\/p>\n<h3 id=\"when-should-i-use-polynomial-regression\"><span class=\"ez-toc-section\" id=\"When_should_I_use_polynomial_regression\"><\/span><b>When should I use polynomial regression?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Use polynomial regression when the relationship between your variables is not linear. It extends linear regression by fitting a polynomial function to the data, which can capture more complex, curvilinear relationships and provide a better model for datasets with non-linear patterns.<\/span><\/p>\n<h3 id=\"what-is-the-difference-between-ridge-and-lasso-regression\"><span class=\"ez-toc-section\" id=\"What_is_the_difference_between_ridge_and_lasso_regression\"><\/span><b>What is the difference between ridge and lasso regression?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Ridge regression adds an L2 penalty to the regression model to handle multicollinearity and stabilise coefficients. Lasso regression adds an L1 penalty, which can shrink some coefficients to zero, effectively performing feature selection. Ridge is for multicollinearity, while Lasso is for feature reduction.<\/span><\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclusion<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">From the above blog, you have learned about Regression Algorithms in Machine Learning. A regression-supervised learning technique helps find correlations between variables. It enables you to predict the continuous output variable based on one or more predictor variables.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Join Pickl.AI for its range of <\/span><a href=\"https:\/\/www.pickl.ai\/\"><span style=\"font-weight: 400;\">Data<\/span> <span style=\"font-weight: 400;\">Science<\/span> <span style=\"font-weight: 400;\">courses,<\/span><\/a><span style=\"font-weight: 400;\"> including Machine Learning and Supervised Learning. You\u2019ll be able to develop your skills and expertise in regression effectively.\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\">\n","protected":false},"excerpt":{"rendered":"Explore 15 regression types in machine learning and learn how to choose the suitable model for your data.\n","protected":false},"author":3,"featured_media":13012,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[2],"tags":[1521,1516,1522,1519,1518,1520,1517],"ppma_author":[2172,2607],"class_list":{"0":"post-4457","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-machine-learning","8":"tag-linear-regression-ml","9":"tag-regression-algorithms","10":"tag-regression-in-machine-learning","11":"tag-regression-machine-learning-algorithms","12":"tag-types-of-regression-in-machine-learning","13":"tag-types-of-regression-models","14":"tag-what-is-regression-in-ml"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v27.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>15 Types of Regression Models in Machine Learning<\/title>\n<meta name=\"description\" content=\"Explore various regression models in machine learning, including linear, polynomial, and ridge regression, to understand uses and applications\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Regression in Machine Learning: Types &amp; Examples\" \/>\n<meta property=\"og:description\" content=\"Explore various regression models in machine learning, including linear, polynomial, and ridge regression, to understand uses and applications\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/\" \/>\n<meta property=\"og:site_name\" content=\"Pickl.AI\" \/>\n<meta property=\"article:published_time\" content=\"2023-08-07T04:11:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-08-02T09:26:01+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image6-2.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Ayush Pareek, Hardik Agrawal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ayush Pareek\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"13 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.pickl.ai\/blog\/regression-in-machine-learning-types-examples\/\"},\"author\":{\"name\":\"Ayush Pareek\",\"@id\":\"https:\/\/www.pickl.ai\/blog\/#\/schema\/person\/a81717c0202750d6049d7d437fbef910\"},\"headline\":\"Regression in Machine Learning: Types &#038; 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