From c8dffec853ce55b690c56d95fd3a008b371c7553 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:17:30 +0530 Subject: [PATCH] Update polynomial-regression.md --- .../machine-learning/polynomial-regression.md | 26 +++++-------------- 1 file changed, 7 insertions(+), 19 deletions(-) diff --git a/contrib/machine-learning/polynomial-regression.md b/contrib/machine-learning/polynomial-regression.md index 3f1287e..d00ede3 100644 --- a/contrib/machine-learning/polynomial-regression.md +++ b/contrib/machine-learning/polynomial-regression.md @@ -1,22 +1,10 @@ # Polynomial Regression -Polynomial Regression is a form of regression analysis in which the relationship between the independent variable \( x \) and the dependent variable \( y \) is modeled as an \( n \)th degree polynomial. This README provides an overview of polynomial regression, including its fundamental concepts, assumptions, and how to implement it using Python. - -## Table of Contents - -1. [Introduction](#introduction) -2. [Concepts](#concepts) -3. [Assumptions](#assumptions) -4. [Implementation](#implementation) - - [Using Scikit-learn](#using-scikit-learn) - - [Code Example](#code-example) -5. [Evaluation Metrics](#evaluation-metrics) -6. [Conclusion](#conclusion) -7. [References](#references) +Polynomial Regression is a form of regression analysis in which the relationship between the independent variable $x$ and the dependent variable $y$ is modeled as an $nth$ degree polynomial. This guide provides an overview of polynomial regression, including its fundamental concepts, assumptions, and how to implement it using Python. ## Introduction -Polynomial Regression is used when the data shows a non-linear relationship between the independent variable \( x \) and the dependent variable \( y \). It extends the simple linear regression model by considering polynomial terms of the independent variable, allowing for a more flexible fit to the data. +Polynomial Regression is used when the data shows a non-linear relationship between the independent variable $x$ and the dependent variable $y$ is modeled as an $nth$ degree polynomial. It extends the simple linear regression model by considering polynomial terms of the independent variable, allowing for a more flexible fit to the data. ## Concepts @@ -29,10 +17,10 @@ $$ $$ Where: -- \( y \) is the dependent variable. -- \( x \) is the independent variable. -- \( \beta_0, \beta_1, \ldots, \beta_n \) are the coefficients of the polynomial. -- \( \epsilon \) is the error term. +- $y$ is the dependent variable. +- $x$ is the independent variable. +- $\beta_0, \beta_1, \ldots, \beta_n$ are the coefficients of the polynomial. +- $\epsilon$ is the error term. ### Degree of Polynomial @@ -111,4 +99,4 @@ Polynomial Regression is a powerful tool for modeling non-linear relationships b ## References - [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/linear_model.html#polynomial-regression) -- [Wikipedia: Polynomial Regression](https://en.wikipedia.org/wiki/Polynomial_reg +- [Wikipedia: Polynomial Regression](https://en.wikipedia.org/wiki/Polynomial_reg)