Category: Regression Analysis In ML

Polynomial Linear Regression is a type of regression analysis in which the relationship between the independent variable and the dependent variable is modeled as an n-th degree polynomial function. Polynomial regression allows for a more complex relationship between the variables to be captured, beyond the linear relationship in Simple and Multiple Linear Regression. Python Implementation…

It is basically the extension of simple linear regression that predicts a response using two or more features. Mathematically we can explain it as follows − Consider a dataset having n observations, p features i.e. independent variables and y as one response i.e. dependent variable the regression line for p features can be calculated as follows − h(xi)=b0+b1xi1+b2xi2+⋅⋅⋅+bpxiph(xi)=b0+b1xi1+b2xi2+⋅⋅⋅+bpxip Here,h(xi)h(xi) is the predicted…

Simple linear regression is a type of regression analysis in which a single independent variable (also known as a predictor variable) is used to predict the dependent variable. In other words, it models the linear relationship between the dependent variable and a single independent variable. Python Implementation Given below is an example that shows how…

Linear regression in machine learning is defined as a statistical model that analyzes the linear relationship between a dependent variable and a given set of independent variables. The linear relationship between variables means that when the value of one or more independent variables will change (increase or decrease), the value of the dependent variable will…

What is Regression Analysis? In machine learning, regression analysis is a statistical technique that predicts continuous numeric values based on the relationship between independent and dependent variables. The main goal of regression analysis is to plot a line or curve that best fit the data and to estimate how one variable affects another. Regression analysis…