Regression in machine learning is a type of supervised learning algorithm. Meaning, the model is trained with the dataset containing known inputs (features) and the known outputs (labels). The main goal of regression is to learn the relationship between given inputs and outputs so that the model can predict the future values accurately. <\/p>\n\n\n\n
Unlike classification, which involves categorizing data into categories (for example, putting data into a \u201cspam\u201d and \u201cnot spam\u201d category, or putting a pet into \u201ccat\u201d or \u201cdog\u201d), regression is used where the outcome is a number. In other words, regression deals with continuous values.<\/p>\n\n\n\n
For example: <\/strong><\/p>\n\n\n\n In these examples, the answer is not a label like \u201cyes\u201d or \u201cno,\u201d but is a number (e.g. \u20b975,00,000 for the house, 32\u00b0C for tomorrow\u2019s temperature, or maybe 15,000 units of sales).<\/p>\n\n\n\n Therefore, regression models in machine learning are fundamentally about identifying patterns in numbers and these will then be used to predict the future. Regression helps answer questions like “How much?” or “How many?” or “What will it be?”<\/p>\n\n\n\n You might be thinking: with so many algorithms out there, why pay particular attention to regression models in machine learning? Here are some reasons:<\/p>\n\n\n\n Linear regression<\/a> is probably the most common algorithm used for predictive analytics. It tries to understand a straight-line relationship between the inputs (features) and outputs (target). Let’s say, it is estimating how much the output value changes when the input changes.<\/p>\n\n\n\n The general formula for linear regression is:<\/em><\/strong><\/p>\n\n\n\n <\/a><\/p>\n\n\n\n Where:<\/em><\/strong><\/p>\n\n\n\n Linear regression is simple to implement and to interpret, which is why it is often the first approach for beginners. However, it may occasionally overfit or underperform if in fact, the data has very strong nonlinear patterns.<\/p>\n\n\n\n Logistic regression<\/a> is a classification algorithm that predicts the probability of an event. It is most commonly used for binary classification, where the target variable has two possible outcomes (e.g., yes\/no, 0\/1, spam\/not spam).<\/p>\n\n\n\n Whereas most regression predicts a continuous value, logistic regression predicts a categorical value by using the sigmoid (logistic) function to transform any real value to the probability of an event from 0 to 1. <\/p>\n\n\n\n Sigmoid Function:<\/em><\/strong><\/p>\n\n\n\n <\/strong><\/a><\/p>\n\n\n\n Here,<\/em><\/strong><\/p>\n\n\n\n If the probability is greater than 0.5<\/strong>, the output is classified as class 1; otherwise, it is classified as class 0.<\/p>\n\n\n\n Note:<\/strong> Although logistic regression is primarily used for classification tasks, it is often grouped with regression algorithms because it relies on regression-like concepts, such as fitting a linear equation to input features and estimating coefficients. <\/em><\/p>\n\n\n\n Sometimes, data does not fit well with a straight line (like in linear regression). Polynomial Regression is an extension of linear regression that adds powers of the input variable to the model. This helps in capturing curved or nonlinear relationships between variables.<\/p>\n\n\n\n Equation:<\/em><\/strong><\/p>\n\n\n\n Y = \u03b2\u2080 + \u03b2\u2081X + \u03b2\u2082X\u00b2 + \u03b2\u2083X\u00b3 + \u2026 + \u03b2\u2099X\u207f + \u03b5<\/em><\/p>\n\n\n\n Here:<\/em><\/strong><\/p>\n\n\n\n Imagine predicting house prices. The relationship between price and size may not be straight; at first, prices rise slowly, then sharply as size increases. Polynomial regression can model this curved growth pattern better than a straight line.<\/p>\n\n\n\n One major issue with linear regression is overfitting, especially when we have many features. Ridge regression in machine learning helps solve this by adding a penalty term (regularization) to the regression equation.<\/p>\n\n\n\n Syntax (cost function):<\/em><\/strong><\/p>\n\n\n\n <\/a><\/p>\n\n\n\n Here, \u03bb (lambda) controls the penalty. Larger values shrink coefficients, reducing model complexity.<\/p>\n\n\n\n Example:<\/strong> Predicting movie ratings from multiple features (genre, actors, budget, runtime) where too many correlated features may overfit.<\/p>\n\n\n\n The loss function in Lasso regression combines two parts: the error term and the penalty term. The error term,<\/a>, measures how far the model\u2019s predictions are from the actual values, just like in linear regression. The penalty term,\u03bb\u2211\u2223\u03b2j\u200b\u2223, adds a cost for having large coefficients. This penalty encourages the model to shrink some coefficients to zero, which means Lasso can automatically remove irrelevant features. In short, Lasso regression helps improve accuracy, prevents overfitting, and performs feature selection at the same time.<\/p>\n\n\n\n Syntax (cost function):<\/em><\/strong><\/p>\n\n\n\n <\/strong><\/a><\/p>\n\n\n\n Where:<\/em><\/strong><\/p>\n\n\n\n ElasticNet combines both Lasso (L1) and Ridge (L2) penalties, making it effective when dealing with datasets that have many features.<\/p>\n\n\n\n Syntax (cost function):<\/em><\/strong><\/p>\n\n\n\n <\/strong><\/a><\/p>\n\n\n\n Where:<\/em><\/strong><\/p>\n\n\n\n Stepwise regression is an iterative technique for constructing regression models. Instead of fitting all of the variables, it adds or deletes predictors based on their statistical significance (for example: p-values or AIC).<\/p>\n\n\n\n The three stepwise methods to create a predictive model are:<\/p>\n\n\n\n (No single formula, but it still uses linear regression equations with selected variables.)<\/p>\n\n\n\n Example: Building a credit scoring model from financial indicators, ensuring that only the most predictive financial indicators are reflected in your model.<\/p>\n\n\n\n Bayesian Regression is a type of regression using Bayesian inference based on bayes\u2019 theorem<\/a>, rather than simply estimating fixed values for the model coefficients. While linear regression produces a single ‘best fit’ line, Bayesian regression treats the model parameters as distributions, rather than single values.<\/p>\n\n\n\n With Bayesian regression, we start with a prior belief (what we believe the parameters to be), and with more data, we update the prior to produce a posterior distribution. This makes Bayesian regression extremely useful when there is uncertainty in data or prior knowledge is available.<\/p>\n\n\n\n Most regression models, such as linear regression, are designed to estimate the mean (or average) of the target variable. However, averages are not always informative because we may want to know how the outcomes behave at other points in the distribution.<\/p>\n\n\n\n Quantile Regression<\/a> helps address this issue because it estimates a quantile (e.g. the 25th, 50th\/median, or 90th percentiles), rather than just estimating the mean. As a result, quantile regression is extremely useful for assessing datasets that are skewed, have outliers, or have unequal distributions.<\/p>\n\n\n\n Example: Suppose you are predicting the price of houses. A simple regression might give you an estimate of the average price, but a buyer or seller may be interested in the lower-end (25th percentile) or higher-end (90th percentile) values of the price. Quantile regression allows us to capture these variations in the data!<\/p>\n\n\n\n Support Vector Machines (SVMs) are known for classification, of course! But SVMs can also be adapted for regression and are called Support Vector Regression (SVR).<\/p>\n\n\n\n In SVR, instead of predicting exact outcomes, you try to fit the data within a margin of tolerance (epsilon), while being as flat as possible.<\/p>\n\n\n\n How does it work? <\/strong><\/p>\n\n\n\n So SVR is less strict and instead pays attention to flatness (simplicity) ignoring all the minuscule deviations.<\/p>\n\n\n\n\n
Why Regression Matters in Machine Learning?<\/strong><\/h2>\n\n\n\n
\n
\n Regression may seem trivial, but it has some interesting facts about it. Did you know that regression is over 200 years old?<\/strong> \n It was first published by Sir Francis Galton<\/strong> while studying heredity. \n Even though it is an old concept, linear regression<\/strong> is still today the most popular algorithm, in both statistics<\/strong> and machine learning<\/strong>.\n<\/div>\n\n\n\nTypes of Regression in Machine Learning<\/strong><\/h2>\n\n\n\n
![\"Types](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/09\/01@2x-1-1200x630.png\" "\\"\\"")
<\/figure>\n\n\n\n1. Linear Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
![\"\"](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/09\/image-1.png\" "\\"\\"")
<\/figure>\n\n\n\n\n
2. Logistic Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
![\"Sigmoid](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/09\/image-2.png\" "\\"\\"")
<\/figure>\n\n\n\n\n
![\"\"](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/09\/02@2x-1-1200x630.png\" "\\"\\"")
<\/figure>\n\n\n\n3. Polynomial Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
\n
4. Ridge Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
![\"Syntax](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/09\/image-5-1200x136.png\" "\\"\\"")
<\/figure>\n\n\n\n5. Lasso Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
![\"Syntax](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/09\/image-3-1200x134.png\" "\\"\\"")
<\/figure>\n\n\n\n\n
6. ElasticNet Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
![\"Syntax](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/09\/image-4-1200x95.png\" "\\"\\"")
<\/figure>\n\n\n\n\n
7. Stepwise Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
\n
8. Bayesian Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
9. Quantile Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
![\"\"](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/09\/03@2x-1-1200x630.png\" "\\"\\"")
<\/figure>\n\n\n\n10. Support Vector Regression in Machine Learning<\/strong><\/h3>\n\n\n\n
\n
\n
Applications of Regression in Machine Learning<\/strong><\/h2>\n\n\n\n
![\"\"](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/09\/04@2x-1-1200x630.png\" "\\"\\"")
<\/figure>\n\n\n\n1. Business and Finance<\/strong><\/h3>\n\n\n\n
\n
2. Healthcare<\/strong><\/h3>\n\n\n\n
\n
3. Marketing<\/strong><\/h3>\n\n\n\n
\n
4. Technology and AI<\/strong><\/h3>\n\n\n\n