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Machine Learning Basics: Polynomial Regression

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In previous stories, I have given a brief of Linear Regression and showed how to perform Simple and Multiple Linear Regression. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.


Introduction to Polynomial Regression Analysis

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Polynomial regression is one of the machine learning algorithms used for making predictions. For example, polynomial regression is widely applied to predict the spread rate of COVID-19 and other infectious diseases. If you would like to learn more about what polynomial regression analysis is, continue reading. Regression analysis is a helpful statistical tool for studying the correlation between two sets of events, or, statistically speaking, variables ― between a dependent variable and one or more independent variables. For example, your weight loss (dependent variable) depends on the number of hours you spend in the gym (independent variable).


Regression Splines in R and Python

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The linear model is named so because of the linear relationship between the input (independent variable) and the output (dependent variable). Even though we know it's of a high probability that the real-world data shows nonlinearity, people usually keep regarding the linear model as one of the top choices. The reasons for that are mainly two things. First, with acceptable approximation, the linear model is one of the simplest models to interpret. Second, the low complexity of the linear model makes it very unlikely to overfit the data, especially when you have small n (sample size) and large p ( variable number).


Polynomial Regression -- explained

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This article requires the knowledge of Linear Regression. If you haven't heard of it, then please check out an article on Linear Regression before you proceed here. Till now we assumed that the relationship between independent variable X and dependent Y can be represented with a straight line. But what if when we can't represent the relationship in a straight line because the data might not be linearly separable? In such kind of scenario we can look for polynomial regression.


Polynomial Regression in Machine Learning

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Polynomial Regression is one of the important parts of Machine Learning. Polynomial Regression is a regression algorithm that frames a relationship between the independent variable(x) and dependent variable(y) as nth degree polynomial. Basically, it brings forth the finest estimation for dependent and independent variables. To convert the multiple linear regression into polynomial regression we need to add some polynomial terms. It acts as a saver when we have to deal with a dataset that is not linearly separable.