![]() ![]() What is a regression line?Ī regression line displays the connection between scattered data points in any set. It’s also useful in business analysis, but what is a regression line exactly and how is it calculated? We’ll take a closer look at the formula and its applications below. Hence, the name is Linear Regression.A Least Squares Regression Line is frequently used in statistics to take a closer look at the relationship between data points. ![]() Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x)). Here, X may be a single feature or multiple features representing the problem. A linear function is the simplest type of function. There are many types of functions or modules that can be used for regression. Here Y is called a dependent or target variable and X is called an independent variable also known as the predictor of Y. The slope of the line indicates how much the dependent variable changes for a unit change in the independent variable(s). The best Fit Line equation provides a straight line that represents the relationship between the dependent and independent variables. There will be the least error in the best-fit line. Our primary objective while using linear regression is to locate the best-fit line, which implies that the error between the predicted and actual values should be kept to a minimum. In regression we have to find the value of Y, So, a function is required that predicts continuous Y in the case of regression given X as independent features. In regression set of records are present with X and Y values and these values are used to learn a function so if you want to predict Y from an unknown X this learned function can be used. The goal of the algorithm is to find the best Fit Line equation that can predict the values based on the independent variables. Elastic Net Regression – Elastic net regression combines the penalties of ridge and lasso regression, offering a balance between their strengths.The equation for lasso regression becomes: Lasso Regression – Lasso regression is another regularization technique that uses an L1 penalty term to shrink the coefficients of less important independent variables towards zero, effectively performing feature selection.The equation for ridge regression becomes: It introduces a penalty term to the least squares objective function, biasing the model towards solutions with smaller coefficients. Ridge Regression – Ridge regression is a regularization technique used to prevent overfitting in linear regression models, especially when dealing with multiple independent variables.It is represented by the general equation: ![]() Polynomial Regression – Polynomial regression goes beyond simple linear regression by incorporating higher-order polynomial terms of the independent variable(s) into the model.X1, X2, …, Xp are the independent variables The equation for multiple linear regression is: This involves more than one independent variable and one dependent variable. ![]() Additionally, linear regression is a cornerstone in assumption testing, enabling researchers to validate key assumptions about the data. Techniques like regularization and support vector machines draw inspiration from linear regression, expanding its utility. Linear regression is not merely a predictive tool it forms the basis for various advanced models. Its simplicity is a virtue, as linear regression is transparent, easy to implement, and serves as a foundational concept for more complex algorithms. The model’s equation provides clear coefficients that elucidate the impact of each independent variable on the dependent variable, facilitating a deeper understanding of the underlying dynamics. The interpretability of linear regression is a notable strength. When the number of the independent feature, is 1 then it is known as Univariate Linear regression, and in the case of more than one feature, it is known as multivariate linear regression. Linear regression is a type of supervised machine learning algorithm that computes the linear relationship between a dependent variable and one or more independent features. Python Implementation of Linear Regression.Evaluation Metrics for Linear Regression.Assumptions of Multiple Linear Regression.Assumptions of Simple Linear Regression.ISRO CS Syllabus for Scientist/Engineer Exam.ISRO CS Original Papers and Official Keys.GATE CS Original Papers and Official Keys.Top 10 System Design Interview Questions and Answers.Top 20 Puzzles Commonly Asked During SDE Interviews.Top 100 DSA Interview Questions Topic-wise. ![]()
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